models add

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/data/processor/pairwise.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import defaultdict
+from typing import TYPE_CHECKING, Any, Optional
+
+from ...extras import logging
+from ...extras.constants import IGNORE_INDEX
+from .processor_utils import DatasetProcessor, infer_seqlen
+
+
+if TYPE_CHECKING:
+    from ..mm_plugin import AudioInput, ImageInput, VideoInput
+
+
+logger = logging.get_logger(__name__)
+
+
+class PairwiseDatasetProcessor(DatasetProcessor):
+    def _encode_data_example(
+        self,
+        prompt: list[dict[str, str]],
+        response: list[dict[str, str]],
+        system: Optional[str],
+        tools: Optional[str],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+    ) -> tuple[list[int], list[int], list[int], list[int]]:
+        chosen_messages = self.template.mm_plugin.process_messages(
+            prompt + [response[0]], images, videos, audios, self.processor
+        )
+        rejected_messages = self.template.mm_plugin.process_messages(
+            prompt + [response[1]], images, videos, audios, self.processor
+        )
+        prompt_ids, chosen_ids = self.template.encode_oneturn(self.tokenizer, chosen_messages, system, tools)
+        _, rejected_ids = self.template.encode_oneturn(self.tokenizer, rejected_messages, system, tools)
+
+        if self.template.efficient_eos:
+            chosen_ids += [self.tokenizer.eos_token_id]
+            rejected_ids += [self.tokenizer.eos_token_id]
+
+        prompt_ids, _ = self.template.mm_plugin.process_token_ids(
+            prompt_ids, None, images, videos, audios, self.tokenizer, self.processor
+        )
+        # consider the response is more important
+        source_len, target_len = infer_seqlen(
+            len(prompt_ids), max(len(chosen_ids), len(rejected_ids)), self.data_args.cutoff_len
+        )
+        prompt_ids = prompt_ids[:source_len]
+        chosen_ids = chosen_ids[:target_len]
+        rejected_ids = rejected_ids[:target_len]
+
+        chosen_input_ids = prompt_ids + chosen_ids
+        chosen_labels = [IGNORE_INDEX] * source_len + chosen_ids
+        rejected_input_ids = prompt_ids + rejected_ids
+        rejected_labels = [IGNORE_INDEX] * source_len + rejected_ids
+        return chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels
+
+    def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+        # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
+        model_inputs = defaultdict(list)
+        for i in range(len(examples["_prompt"])):
+            if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
+                logger.warning_rank0(
+                    "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
+                )
+                continue
+
+            chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels = self._encode_data_example(
+                prompt=examples["_prompt"][i],
+                response=examples["_response"][i],
+                system=examples["_system"][i],
+                tools=examples["_tools"][i],
+                images=examples["_images"][i] or [],
+                videos=examples["_videos"][i] or [],
+                audios=examples["_audios"][i] or [],
+            )
+            model_inputs["chosen_input_ids"].append(chosen_input_ids)
+            model_inputs["chosen_attention_mask"].append([1] * len(chosen_input_ids))
+            model_inputs["chosen_labels"].append(chosen_labels)
+            model_inputs["rejected_input_ids"].append(rejected_input_ids)
+            model_inputs["rejected_attention_mask"].append([1] * len(rejected_input_ids))
+            model_inputs["rejected_labels"].append(rejected_labels)
+            model_inputs["images"].append(examples["_images"][i])
+            model_inputs["videos"].append(examples["_videos"][i])
+            model_inputs["audios"].append(examples["_audios"][i])
+
+        return model_inputs
+
+    def print_data_example(self, example: dict[str, list[int]]) -> None:
+        valid_chosen_labels = list(filter(lambda x: x != IGNORE_INDEX, example["chosen_labels"]))
+        valid_rejected_labels = list(filter(lambda x: x != IGNORE_INDEX, example["rejected_labels"]))
+        print("chosen_input_ids:\n{}".format(example["chosen_input_ids"]))
+        print(
+            "chosen_inputs:\n{}".format(self.tokenizer.decode(example["chosen_input_ids"], skip_special_tokens=False))
+        )
+        print("chosen_label_ids:\n{}".format(example["chosen_labels"]))
+        print(f"chosen_labels:\n{self.tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)}")
+        print("rejected_input_ids:\n{}".format(example["rejected_input_ids"]))
+        print(
+            "rejected_inputs:\n{}".format(
+                self.tokenizer.decode(example["rejected_input_ids"], skip_special_tokens=False)
+            )
+        )
+        print("rejected_label_ids:\n{}".format(example["rejected_labels"]))
+        print(f"rejected_labels:\n{self.tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)}")

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/data/processor/pretrain.py

+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from itertools import chain
+from typing import Any
+
+from .processor_utils import DatasetProcessor
+
+
+@dataclass
+class PretrainDatasetProcessor(DatasetProcessor):
+    def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+        # build grouped texts with format `X1 X2 X3 ...` if packing is enabled
+        eos_token = "<|end_of_text|>" if self.data_args.template == "llama3" else self.tokenizer.eos_token
+        text_examples = [messages[0]["content"] + eos_token for messages in examples["_prompt"]]
+
+        if not self.data_args.packing:
+            if getattr(self.tokenizer, "add_bos_token", False):
+                text_examples = [self.tokenizer.bos_token + example for example in text_examples]
+
+            result = self.tokenizer(
+                text_examples, add_special_tokens=False, truncation=True, max_length=self.data_args.cutoff_len
+            )
+        else:
+            tokenized_examples = self.tokenizer(text_examples, add_special_tokens=False)
+            concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
+            total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
+            block_size = self.data_args.cutoff_len
+            total_length = (total_length // block_size) * block_size
+            result = {
+                k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+                for k, t in concatenated_examples.items()
+            }
+            if getattr(self.tokenizer, "add_bos_token", False):
+                for i in range(len(result["input_ids"])):
+                    result["input_ids"][i][0] = self.tokenizer.bos_token_id
+
+        return result
+
+    def print_data_example(self, example: dict[str, list[int]]) -> None:
+        print("input_ids:\n{}".format(example["input_ids"]))
+        print("inputs:\n{}".format(self.tokenizer.decode(example["input_ids"], skip_special_tokens=False)))

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/data/processor/processor_utils.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import bisect
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Optional
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer, ProcessorMixin
+
+    from ...hparams import DataArguments
+    from ..template import Template
+
+
+@dataclass
+class DatasetProcessor(ABC):
+    r"""A class for data processors."""
+
+    template: "Template"
+    tokenizer: "PreTrainedTokenizer"
+    processor: Optional["ProcessorMixin"]
+    data_args: "DataArguments"
+
+    @abstractmethod
+    def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+        r"""Build model inputs from the examples."""
+        ...
+
+    @abstractmethod
+    def print_data_example(self, example: dict[str, list[int]]) -> None:
+        r"""Print a data example to stdout."""
+        ...
+
+
+def search_for_fit(numbers: list[int], capacity: int) -> int:
+    r"""Find the index of largest number that fits into the knapsack with the given capacity."""
+    index = bisect.bisect(numbers, capacity)
+    return -1 if index == 0 else (index - 1)
+
+
+def greedy_knapsack(numbers: list[int], capacity: int) -> list[list[int]]:
+    r"""Implement efficient greedy algorithm with binary search for the knapsack problem."""
+    numbers.sort()  # sort numbers in ascending order for binary search
+    knapsacks = []
+
+    while numbers:
+        current_knapsack = []
+        remaining_capacity = capacity
+
+        while True:
+            index = search_for_fit(numbers, remaining_capacity)
+            if index == -1:
+                break  # no more numbers fit in this knapsack
+
+            remaining_capacity -= numbers[index]  # update the remaining capacity
+            current_knapsack.append(numbers.pop(index))  # add the number to knapsack
+
+        knapsacks.append(current_knapsack)
+
+    return knapsacks
+
+
+def infer_seqlen(source_len: int, target_len: int, cutoff_len: int) -> tuple[int, int]:
+    r"""Compute the real sequence length after truncation by the cutoff_len."""
+    if target_len * 2 < cutoff_len:  # truncate source
+        max_target_len = cutoff_len
+    elif source_len * 2 < cutoff_len:  # truncate target
+        max_target_len = cutoff_len - source_len
+    else:  # truncate both
+        max_target_len = int(cutoff_len * (target_len / (source_len + target_len)))
+
+    new_target_len = min(max_target_len, target_len)
+    max_source_len = max(cutoff_len - new_target_len, 0)
+    new_source_len = min(max_source_len, source_len)
+    return new_source_len, new_target_len

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/data/processor/supervised.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Optional
+
+from ...extras import logging
+from ...extras.constants import IGNORE_INDEX
+from .processor_utils import DatasetProcessor, greedy_knapsack, infer_seqlen
+
+
+if TYPE_CHECKING:
+    from ..mm_plugin import AudioInput, ImageInput, VideoInput
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class SupervisedDatasetProcessor(DatasetProcessor):
+    def _encode_data_example(
+        self,
+        prompt: list[dict[str, str]],
+        response: list[dict[str, str]],
+        system: Optional[str],
+        tools: Optional[str],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+    ) -> tuple[list[int], list[int]]:
+        messages = self.template.mm_plugin.process_messages(prompt + response, images, videos, audios, self.processor)
+        input_ids, labels = self.template.mm_plugin.process_token_ids(
+            [], [], images, videos, audios, self.tokenizer, self.processor
+        )
+        encoded_pairs = self.template.encode_multiturn(self.tokenizer, messages, system, tools)
+        total_length = len(input_ids) + (1 if self.template.efficient_eos else 0)
+        if self.data_args.mask_history:
+            encoded_pairs = encoded_pairs[::-1]  # high priority for last turns
+
+        for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs):
+            if total_length >= self.data_args.cutoff_len:
+                break
+
+            source_len, target_len = infer_seqlen(
+                len(source_ids), len(target_ids), self.data_args.cutoff_len - total_length
+            )
+            source_ids = source_ids[:source_len]
+            target_ids = target_ids[:target_len]
+            total_length += source_len + target_len
+
+            if self.data_args.train_on_prompt:
+                source_label = source_ids
+            elif self.template.efficient_eos:
+                source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
+            else:
+                source_label = [IGNORE_INDEX] * source_len
+
+            if self.data_args.mask_history and turn_idx != 0:  # train on the last turn only
+                target_label = [IGNORE_INDEX] * target_len
+            else:
+                target_label = target_ids
+
+            if self.data_args.mask_history:  # reversed sequences
+                input_ids = source_ids + target_ids + input_ids
+                labels = source_label + target_label + labels
+            else:
+                input_ids += source_ids + target_ids
+                labels += source_label + target_label
+
+        if self.template.efficient_eos:
+            input_ids += [self.tokenizer.eos_token_id]
+            labels += [self.tokenizer.eos_token_id]
+
+        return input_ids, labels
+
+    def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+        # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
+        # for multiturn examples, we only mask the prompt part in each prompt-response pair.
+        model_inputs = defaultdict(list)
+        for i in range(len(examples["_prompt"])):
+            if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
+                logger.warning_rank0(
+                    "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
+                )
+                continue
+
+            input_ids, labels = self._encode_data_example(
+                prompt=examples["_prompt"][i],
+                response=examples["_response"][i],
+                system=examples["_system"][i],
+                tools=examples["_tools"][i],
+                images=examples["_images"][i] or [],
+                videos=examples["_videos"][i] or [],
+                audios=examples["_audios"][i] or [],
+            )
+            model_inputs["input_ids"].append(input_ids)
+            model_inputs["attention_mask"].append([1] * len(input_ids))
+            model_inputs["labels"].append(labels)
+            model_inputs["images"].append(examples["_images"][i])
+            model_inputs["videos"].append(examples["_videos"][i])
+            model_inputs["audios"].append(examples["_audios"][i])
+
+        return model_inputs
+
+    def print_data_example(self, example: dict[str, list[int]]) -> None:
+        valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"]))
+        print("input_ids:\n{}".format(example["input_ids"]))
+        print("inputs:\n{}".format(self.tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
+        print("label_ids:\n{}".format(example["labels"]))
+        print(f"labels:\n{self.tokenizer.decode(valid_labels, skip_special_tokens=False)}")
+
+
+@dataclass
+class PackedSupervisedDatasetProcessor(SupervisedDatasetProcessor):
+    def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+        # TODO: use `position_ids` to achieve packing
+        # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
+        # and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
+        valid_num = 0
+        batch_input_ids, batch_labels, batch_images, batch_videos, batch_audios = [], [], [], [], []
+        lengths = []
+        length2indexes = defaultdict(list)
+        for i in range(len(examples["_prompt"])):
+            if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
+                logger.warning_rank0(
+                    "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
+                )
+                continue
+
+            input_ids, labels = self._encode_data_example(
+                prompt=examples["_prompt"][i],
+                response=examples["_response"][i],
+                system=examples["_system"][i],
+                tools=examples["_tools"][i],
+                images=examples["_images"][i] or [],
+                videos=examples["_videos"][i] or [],
+                audios=examples["_audios"][i] or [],
+            )
+            length = len(input_ids)
+            if length > self.data_args.cutoff_len:
+                logger.warning_rank0(f"Dropped lengthy example with length {length} > {self.data_args.cutoff_len}.")
+            else:
+                lengths.append(length)
+                length2indexes[length].append(valid_num)
+                batch_input_ids.append(input_ids)
+                batch_labels.append(labels)
+                batch_images.append(examples["_images"][i] or [])
+                batch_videos.append(examples["_videos"][i] or [])
+                batch_audios.append(examples["_audios"][i] or [])
+                valid_num += 1
+
+        model_inputs = defaultdict(list)
+        knapsacks = greedy_knapsack(lengths, self.data_args.cutoff_len)
+        for knapsack in knapsacks:
+            packed_input_ids, packed_attention_masks, packed_position_ids, packed_labels = [], [], [], []
+            packed_images, packed_videos, packed_audios = [], [], []
+            for i, length in enumerate(knapsack):
+                index = length2indexes[length].pop()
+                packed_input_ids += batch_input_ids[index]
+                packed_position_ids += list(range(len(batch_input_ids[index])))  # NOTE: pad_to_multiple_of ignore this
+                packed_labels += batch_labels[index]
+                packed_images += batch_images[index]
+                packed_videos += batch_videos[index]
+                packed_audios += batch_audios[index]
+                if self.data_args.neat_packing:
+                    packed_attention_masks += [i + 1] * len(batch_input_ids[index])  # start from 1
+                else:
+                    packed_attention_masks += [1] * len(batch_input_ids[index])
+
+            if len(packed_input_ids) < self.data_args.cutoff_len + 1:  # avoid flash_attn drops attn mask
+                pad_length = self.data_args.cutoff_len - len(packed_input_ids) + 1
+                packed_input_ids += [self.tokenizer.pad_token_id] * pad_length
+                packed_position_ids += [0] * pad_length
+                packed_labels += [IGNORE_INDEX] * pad_length
+                if self.data_args.neat_packing:
+                    packed_attention_masks += [0] * pad_length
+                else:
+                    packed_attention_masks += [1] * pad_length  # more efficient flash_attn
+
+            if len(packed_input_ids) != self.data_args.cutoff_len + 1:
+                raise ValueError("The length of packed example should be identical to the cutoff length.")
+
+            model_inputs["input_ids"].append(packed_input_ids)
+            model_inputs["attention_mask"].append(packed_attention_masks)
+            model_inputs["position_ids"].append(packed_position_ids)
+            model_inputs["labels"].append(packed_labels)
+            model_inputs["images"].append(packed_images or None)
+            model_inputs["videos"].append(packed_videos or None)
+            model_inputs["audios"].append(packed_audios or None)
+
+        return model_inputs

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/data/processor/unsupervised.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import defaultdict
+from typing import TYPE_CHECKING, Any, Optional
+
+from ...extras import logging
+from ..data_utils import Role
+from .processor_utils import DatasetProcessor, infer_seqlen
+
+
+if TYPE_CHECKING:
+    from ..mm_plugin import AudioInput, ImageInput, VideoInput
+
+
+logger = logging.get_logger(__name__)
+
+
+class UnsupervisedDatasetProcessor(DatasetProcessor):
+    def _encode_data_example(
+        self,
+        prompt: list[dict[str, str]],
+        response: list[dict[str, str]],
+        system: Optional[str],
+        tools: Optional[str],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+    ) -> tuple[list[int], list[int]]:
+        if len(response) == 1:
+            messages = prompt + response
+        else:
+            messages = prompt + [{"role": Role.ASSISTANT.value, "content": ""}]
+
+        messages = self.template.mm_plugin.process_messages(messages, images, videos, audios, self.processor)
+        input_ids, labels = self.template.encode_oneturn(self.tokenizer, messages, system, tools)
+        if self.template.efficient_eos:
+            labels += [self.tokenizer.eos_token_id]
+
+        input_ids, _ = self.template.mm_plugin.process_token_ids(
+            input_ids, None, images, videos, audios, self.tokenizer, self.processor
+        )
+        source_len, target_len = infer_seqlen(len(input_ids), len(labels), self.data_args.cutoff_len)
+        input_ids = input_ids[:source_len]
+        labels = labels[:target_len]
+        return input_ids, labels
+
+    def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+        # build inputs with format `<bos> X` and labels with format `Y <eos>`
+        model_inputs = defaultdict(list)
+        for i in range(len(examples["_prompt"])):
+            if len(examples["_prompt"][i]) % 2 != 1:
+                logger.warning_rank0(
+                    "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
+                )
+                continue
+
+            input_ids, labels = self._encode_data_example(
+                prompt=examples["_prompt"][i],
+                response=examples["_response"][i],
+                system=examples["_system"][i],
+                tools=examples["_tools"][i],
+                images=examples["_images"][i] or [],
+                videos=examples["_videos"][i] or [],
+                audios=examples["_audios"][i] or [],
+            )
+            model_inputs["input_ids"].append(input_ids)
+            model_inputs["attention_mask"].append([1] * len(input_ids))
+            model_inputs["labels"].append(labels)
+            model_inputs["images"].append(examples["_images"][i])
+            model_inputs["videos"].append(examples["_videos"][i])
+            model_inputs["audios"].append(examples["_audios"][i])
+
+        return model_inputs
+
+    def print_data_example(self, example: dict[str, list[int]]) -> None:
+        print("input_ids:\n{}".format(example["input_ids"]))
+        print("inputs:\n{}".format(self.tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
+        print("label_ids:\n{}".format(example["labels"]))
+        print("labels:\n{}".format(self.tokenizer.decode(example["labels"], skip_special_tokens=False)))

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/data/template.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional, Union
+
+from typing_extensions import override
+
+from ..extras import logging
+from .data_utils import Role
+from .formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
+from .mm_plugin import get_mm_plugin
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+
+    from ..hparams import DataArguments
+    from .formatter import SLOTS, Formatter
+    from .mm_plugin import BasePlugin
+    from .tool_utils import FunctionCall
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class Template:
+    format_user: "Formatter"
+    format_assistant: "Formatter"
+    format_system: "Formatter"
+    format_function: "Formatter"
+    format_observation: "Formatter"
+    format_tools: "Formatter"
+    format_prefix: "Formatter"
+    default_system: str
+    stop_words: list[str]
+    thought_words: tuple[str, str]
+    efficient_eos: bool
+    replace_eos: bool
+    replace_jinja_template: bool
+    enable_thinking: Optional[bool]
+    mm_plugin: "BasePlugin"
+
+    def encode_oneturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        messages: list[dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+    ) -> tuple[list[int], list[int]]:
+        r"""Return a single pair of token ids representing prompt and response respectively."""
+        encoded_messages = self._encode(tokenizer, messages, system, tools)
+        prompt_ids = []
+        for encoded_ids in encoded_messages[:-1]:
+            prompt_ids += encoded_ids
+
+        response_ids = encoded_messages[-1]
+        return prompt_ids, response_ids
+
+    def encode_multiturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        messages: list[dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+    ) -> list[tuple[list[int], list[int]]]:
+        r"""Return multiple pairs of token ids representing prompts and responses respectively."""
+        encoded_messages = self._encode(tokenizer, messages, system, tools)
+        return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
+
+    def extract_tool(self, content: str) -> Union[str, list["FunctionCall"]]:
+        r"""Extract tool message."""
+        return self.format_tools.extract(content)
+
+    def get_stop_token_ids(self, tokenizer: "PreTrainedTokenizer") -> list[int]:
+        r"""Return stop token ids."""
+        stop_token_ids = {tokenizer.eos_token_id}
+        for token in self.stop_words:
+            stop_token_ids.add(tokenizer.convert_tokens_to_ids(token))
+
+        return list(stop_token_ids)
+
+    def add_thought(self, content: str = "") -> str:
+        r"""Add empty thought to assistant message."""
+        return f"{self.thought_words[0]}\n\n{self.thought_words[1]}\n\n" + content
+
+    def remove_thought(self, content: str) -> str:
+        r"""Remove thought from assistant message."""
+        pattern = re.compile(f"{re.escape(self.thought_words[0])}(.*?){re.escape(self.thought_words[1])}", re.DOTALL)
+        return re.sub(pattern, "", content).lstrip("\n")
+
+    def get_thought_word_ids(self, tokenizer: "PreTrainedTokenizer") -> list[int]:
+        r"""Get the token ids of thought words."""
+        return tokenizer.encode(self.add_thought(), add_special_tokens=False)
+
+    def _convert_elements_to_ids(self, tokenizer: "PreTrainedTokenizer", elements: "SLOTS") -> list[int]:
+        r"""Convert elements to token ids."""
+        token_ids = []
+        for elem in elements:
+            if isinstance(elem, str):
+                if len(elem) != 0:
+                    token_ids += tokenizer.encode(elem, add_special_tokens=False)
+            elif isinstance(elem, dict):
+                token_ids += [tokenizer.convert_tokens_to_ids(elem.get("token"))]
+            elif isinstance(elem, set):
+                if "bos_token" in elem and tokenizer.bos_token_id is not None:
+                    token_ids += [tokenizer.bos_token_id]
+                elif "eos_token" in elem and tokenizer.eos_token_id is not None:
+                    token_ids += [tokenizer.eos_token_id]
+            else:
+                raise ValueError(f"Input must be string, set[str] or dict[str, str], got {type(elem)}")
+
+        return token_ids
+
+    def _encode(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        messages: list[dict[str, str]],
+        system: Optional[str],
+        tools: Optional[str],
+    ) -> list[list[int]]:
+        r"""Encode formatted inputs to pairs of token ids.
+
+        Turn 0: prefix + system + query        resp
+        Turn t: query                          resp.
+        """
+        system = system or self.default_system
+        encoded_messages = []
+        for i, message in enumerate(messages):
+            elements = []
+
+            if i == 0:
+                elements += self.format_prefix.apply()
+                if system or tools:
+                    tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
+                    elements += self.format_system.apply(content=(system + tool_text))
+
+            if message["role"] == Role.USER:
+                elements += self.format_user.apply(content=message["content"], idx=str(i // 2))
+            elif message["role"] == Role.ASSISTANT:
+                elements += self.format_assistant.apply(content=message["content"])
+            elif message["role"] == Role.OBSERVATION:
+                elements += self.format_observation.apply(content=message["content"])
+            elif message["role"] == Role.FUNCTION:
+                elements += self.format_function.apply(content=message["content"])
+            else:
+                raise NotImplementedError("Unexpected role: {}".format(message["role"]))
+
+            encoded_messages.append(self._convert_elements_to_ids(tokenizer, elements))
+
+        return encoded_messages
+
+    @staticmethod
+    def _add_or_replace_eos_token(tokenizer: "PreTrainedTokenizer", eos_token: str) -> None:
+        r"""Add or replace eos token to the tokenizer."""
+        if tokenizer.eos_token == eos_token:
+            return
+
+        is_added = tokenizer.eos_token_id is None
+        num_added_tokens = tokenizer.add_special_tokens({"eos_token": eos_token})
+
+        if is_added:
+            logger.info_rank0(f"Add eos token: {tokenizer.eos_token}.")
+        else:
+            logger.info_rank0(f"Replace eos token: {tokenizer.eos_token}.")
+
+        if num_added_tokens > 0:
+            logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
+
+    def fix_special_tokens(self, tokenizer: "PreTrainedTokenizer") -> None:
+        r"""Add eos token and pad token to the tokenizer."""
+        stop_words = self.stop_words
+        if self.replace_eos:
+            if not stop_words:
+                raise ValueError("Stop words are required to replace the EOS token.")
+
+            self._add_or_replace_eos_token(tokenizer, eos_token=stop_words[0])
+            stop_words = stop_words[1:]
+
+        if tokenizer.eos_token_id is None:
+            self._add_or_replace_eos_token(tokenizer, eos_token="<|endoftext|>")
+
+        if tokenizer.pad_token_id is None:
+            tokenizer.pad_token = tokenizer.eos_token
+            logger.info_rank0(f"Add pad token: {tokenizer.pad_token}")
+
+        if stop_words:
+            num_added_tokens = tokenizer.add_special_tokens(
+                dict(additional_special_tokens=stop_words), replace_additional_special_tokens=False
+            )
+            logger.info_rank0("Add {} to stop words.".format(",".join(stop_words)))
+            if num_added_tokens > 0:
+                logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
+
+    @staticmethod
+    def _jinja_escape(content: str) -> str:
+        r"""Escape single quotes in content."""
+        return content.replace("'", r"\'")
+
+    @staticmethod
+    def _convert_slots_to_jinja(slots: "SLOTS", tokenizer: "PreTrainedTokenizer", placeholder: str = "content") -> str:
+        r"""Convert slots to jinja template."""
+        slot_items = []
+        for slot in slots:
+            if isinstance(slot, str):
+                slot_pieces = slot.split("{{content}}")
+                if slot_pieces[0]:
+                    slot_items.append("'" + Template._jinja_escape(slot_pieces[0]) + "'")
+                if len(slot_pieces) > 1:
+                    slot_items.append(placeholder)
+                    if slot_pieces[1]:
+                        slot_items.append("'" + Template._jinja_escape(slot_pieces[1]) + "'")
+            elif isinstance(slot, set):  # do not use {{ eos_token }} since it may be replaced
+                if "bos_token" in slot and tokenizer.bos_token_id is not None:
+                    slot_items.append("'" + tokenizer.bos_token + "'")
+                elif "eos_token" in slot and tokenizer.eos_token_id is not None:
+                    slot_items.append("'" + tokenizer.eos_token + "'")
+            elif isinstance(slot, dict):
+                raise ValueError("Dict is not supported.")
+
+        return " + ".join(slot_items)
+
+    def _get_jinja_template(self, tokenizer: "PreTrainedTokenizer") -> str:
+        r"""Return the jinja template."""
+        prefix = self._convert_slots_to_jinja(self.format_prefix.apply(), tokenizer)
+        system = self._convert_slots_to_jinja(self.format_system.apply(), tokenizer, placeholder="system_message")
+        user = self._convert_slots_to_jinja(self.format_user.apply(), tokenizer)
+        assistant = self._convert_slots_to_jinja(self.format_assistant.apply(), tokenizer)
+        jinja_template = ""
+        if prefix:
+            jinja_template += "{{ " + prefix + " }}"
+
+        if self.default_system:
+            jinja_template += "{% set system_message = '" + self._jinja_escape(self.default_system) + "' %}"
+
+        jinja_template += (
+            "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}"
+            "{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}"
+            "{% if system_message is defined %}{{ " + system + " }}{% endif %}"
+            "{% for message in loop_messages %}"
+            "{% set content = message['content'] %}"
+            "{% if message['role'] == 'user' %}"
+            "{{ " + user + " }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ " + assistant + " }}"
+            "{% endif %}"
+            "{% endfor %}"
+        )
+        return jinja_template
+
+    def fix_jinja_template(self, tokenizer: "PreTrainedTokenizer") -> None:
+        r"""Replace the jinja template in the tokenizer."""
+        if tokenizer.chat_template is None or self.replace_jinja_template:
+            try:
+                tokenizer.chat_template = self._get_jinja_template(tokenizer)
+            except ValueError as e:
+                logger.info_rank0(f"Cannot add this chat template to tokenizer: {e}.")
+
+    @staticmethod
+    def _convert_slots_to_ollama(
+        slots: "SLOTS", tokenizer: "PreTrainedTokenizer", placeholder: str = "content"
+    ) -> str:
+        r"""Convert slots to ollama template."""
+        slot_items = []
+        for slot in slots:
+            if isinstance(slot, str):
+                slot_pieces = slot.split("{{content}}")
+                if slot_pieces[0]:
+                    slot_items.append(slot_pieces[0])
+                if len(slot_pieces) > 1:
+                    slot_items.append("{{ " + placeholder + " }}")
+                    if slot_pieces[1]:
+                        slot_items.append(slot_pieces[1])
+            elif isinstance(slot, set):  # do not use {{ eos_token }} since it may be replaced
+                if "bos_token" in slot and tokenizer.bos_token_id is not None:
+                    slot_items.append(tokenizer.bos_token)
+                elif "eos_token" in slot and tokenizer.eos_token_id is not None:
+                    slot_items.append(tokenizer.eos_token)
+            elif isinstance(slot, dict):
+                raise ValueError("Dict is not supported.")
+
+        return "".join(slot_items)
+
+    def _get_ollama_template(self, tokenizer: "PreTrainedTokenizer") -> str:
+        r"""Return the ollama template."""
+        prefix = self._convert_slots_to_ollama(self.format_prefix.apply(), tokenizer)
+        system = self._convert_slots_to_ollama(self.format_system.apply(), tokenizer, placeholder=".System")
+        user = self._convert_slots_to_ollama(self.format_user.apply(), tokenizer, placeholder=".Content")
+        assistant = self._convert_slots_to_ollama(self.format_assistant.apply(), tokenizer, placeholder=".Content")
+        return (
+            f"{prefix}{{{{ if .System }}}}{system}{{{{ end }}}}"
+            f"""{{{{ range .Messages }}}}{{{{ if eq .Role "user" }}}}{user}"""
+            f"""{{{{ else if eq .Role "assistant" }}}}{assistant}{{{{ end }}}}{{{{ end }}}}"""
+        )
+
+    def get_ollama_modelfile(self, tokenizer: "PreTrainedTokenizer") -> str:
+        r"""Return the ollama modelfile.
+
+        TODO: support function calling.
+        """
+        modelfile = "# ollama modelfile auto-generated by llamafactory\n\n"
+        modelfile += f'FROM .\n\nTEMPLATE """{self._get_ollama_template(tokenizer)}"""\n\n'
+
+        if self.default_system:
+            modelfile += f'SYSTEM """{self.default_system}"""\n\n'
+
+        for stop_token_id in self.get_stop_token_ids(tokenizer):
+            modelfile += f'PARAMETER stop "{tokenizer.convert_ids_to_tokens(stop_token_id)}"\n'
+
+        modelfile += "PARAMETER num_ctx 4096\n"
+        return modelfile
+
+
+@dataclass
+class Llama2Template(Template):
+    r"""A template that fuse the system message to first user message."""
+
+    @override
+    def _encode(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        messages: list[dict[str, str]],
+        system: str,
+        tools: str,
+    ) -> list[list[int]]:
+        system = system or self.default_system
+        encoded_messages = []
+        for i, message in enumerate(messages):
+            elements = []
+
+            system_text = ""
+            if i == 0:
+                elements += self.format_prefix.apply()
+                if system or tools:
+                    tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
+                    system_text = self.format_system.apply(content=(system + tool_text))[0]
+
+            if message["role"] == Role.USER:
+                elements += self.format_user.apply(content=system_text + message["content"])
+            elif message["role"] == Role.ASSISTANT:
+                elements += self.format_assistant.apply(content=message["content"])
+            elif message["role"] == Role.OBSERVATION:
+                elements += self.format_observation.apply(content=message["content"])
+            elif message["role"] == Role.FUNCTION:
+                elements += self.format_function.apply(content=message["content"])
+            else:
+                raise NotImplementedError("Unexpected role: {}".format(message["role"]))
+
+            encoded_messages.append(self._convert_elements_to_ids(tokenizer, elements))
+
+        return encoded_messages
+
+    def _get_jinja_template(self, tokenizer: "PreTrainedTokenizer") -> str:
+        prefix = self._convert_slots_to_jinja(self.format_prefix.apply(), tokenizer)
+        system_message = self._convert_slots_to_jinja(
+            self.format_system.apply(), tokenizer, placeholder="system_message"
+        )
+        user_message = self._convert_slots_to_jinja(self.format_user.apply(), tokenizer)
+        assistant_message = self._convert_slots_to_jinja(self.format_assistant.apply(), tokenizer)
+        jinja_template = ""
+        if prefix:
+            jinja_template += "{{ " + prefix + " }}"
+
+        if self.default_system:
+            jinja_template += "{% set system_message = '" + self._jinja_escape(self.default_system) + "' %}"
+
+        jinja_template += (
+            "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}"
+            "{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}"
+            "{% for message in loop_messages %}"
+            "{% if loop.index0 == 0 and system_message is defined %}"
+            "{% set content = " + system_message + " + message['content'] %}"
+            "{% else %}{% set content = message['content'] %}{% endif %}"
+            "{% if message['role'] == 'user' %}"
+            "{{ " + user_message + " }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ " + assistant_message + " }}"
+            "{% endif %}"
+            "{% endfor %}"
+        )
+        return jinja_template
+
+
+@dataclass
+class ReasoningTemplate(Template):
+    r"""A template that add thought to assistant message."""
+
+    @override
+    def encode_oneturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        messages: list[dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+    ) -> tuple[list[int], list[int]]:
+        messages = deepcopy(messages)
+        for i in range(1, len(messages) - 2, 2):
+            messages[i]["content"] = self.remove_thought(messages[i]["content"])
+
+        if self.enable_thinking is False:  # remove all cot
+            messages[-1]["content"] = self.remove_thought(messages[-1]["content"])
+
+        prompt_ids, response_ids = super().encode_oneturn(tokenizer, messages, system, tools)
+        if (
+            self.thought_words[0] not in messages[-1]["content"]
+            and self.thought_words[1] not in messages[-1]["content"]
+        ):  # add empty cot
+            if not self.enable_thinking:  # do not compute loss
+                prompt_ids += self.get_thought_word_ids(tokenizer)
+            else:  # do compute loss
+                response_ids = self.get_thought_word_ids(tokenizer) + response_ids
+
+        return prompt_ids, response_ids
+
+    @override
+    def encode_multiturn(
+        self,
+        tokenizer: "PreTrainedTokenizer",
+        messages: list[dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+    ) -> list[tuple[list[int], list[int]]]:
+        messages = deepcopy(messages)
+        if self.enable_thinking is False:  # remove all cot
+            for i in range(1, len(messages), 2):
+                messages[i]["content"] = self.remove_thought(messages[i]["content"])
+
+        encoded_messages = self._encode(tokenizer, messages, system, tools)
+        for i in range(0, len(messages), 2):
+            if (
+                self.thought_words[0] not in messages[i + 1]["content"]
+                and self.thought_words[1] not in messages[i + 1]["content"]
+            ):  # add empty cot
+                if not self.enable_thinking:  # do not compute loss
+                    encoded_messages[i] += self.get_thought_word_ids(tokenizer)
+                else:  # do compute loss
+                    encoded_messages[i + 1] = self.get_thought_word_ids(tokenizer) + encoded_messages[i + 1]
+
+        return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
+
+
+TEMPLATES: dict[str, "Template"] = {}
+
+
+def register_template(
+    name: str,
+    format_user: Optional["Formatter"] = None,
+    format_assistant: Optional["Formatter"] = None,
+    format_system: Optional["Formatter"] = None,
+    format_function: Optional["Formatter"] = None,
+    format_observation: Optional["Formatter"] = None,
+    format_tools: Optional["Formatter"] = None,
+    format_prefix: Optional["Formatter"] = None,
+    default_system: str = "",
+    stop_words: Optional[list[str]] = None,
+    thought_words: Optional[tuple[str, str]] = None,
+    efficient_eos: bool = False,
+    replace_eos: bool = False,
+    replace_jinja_template: bool = False,
+    enable_thinking: Optional[bool] = True,
+    mm_plugin: "BasePlugin" = get_mm_plugin(name="base"),
+    template_class: type["Template"] = Template,
+) -> None:
+    r"""Register a chat template.
+
+    To add the following chat template:
+    ```
+    <s><user>user prompt here
+    <model>model response here</s>
+    <user>user prompt here
+    <model>model response here</s>
+    ```
+
+    The corresponding code should be:
+    ```
+    register_template(
+        name="custom",
+        format_user=StringFormatter(slots=["<user>{{content}}\n<model>"]),
+        format_assistant=StringFormatter(slots=["{{content}}</s>\n"]),
+        format_prefix=EmptyFormatter("<s>"),
+    )
+    ```
+    """
+    if name in TEMPLATES:
+        raise ValueError(f"Template {name} already exists.")
+
+    default_slots = ["{{content}}"] if efficient_eos else ["{{content}}", {"eos_token"}]
+    default_user_formatter = StringFormatter(slots=["{{content}}"])
+    default_assistant_formatter = StringFormatter(slots=default_slots)
+    if format_assistant is not None:
+        default_function_formatter = FunctionFormatter(slots=format_assistant.slots, tool_format="default")
+    else:
+        default_function_formatter = FunctionFormatter(slots=default_slots, tool_format="default")
+
+    default_tool_formatter = ToolFormatter(tool_format="default")
+    default_prefix_formatter = EmptyFormatter()
+    TEMPLATES[name] = template_class(
+        format_user=format_user or default_user_formatter,
+        format_assistant=format_assistant or default_assistant_formatter,
+        format_system=format_system or default_user_formatter,
+        format_function=format_function or default_function_formatter,
+        format_observation=format_observation or format_user or default_user_formatter,
+        format_tools=format_tools or default_tool_formatter,
+        format_prefix=format_prefix or default_prefix_formatter,
+        default_system=default_system,
+        stop_words=stop_words or [],
+        thought_words=thought_words or ("<think>", "</think>"),
+        efficient_eos=efficient_eos,
+        replace_eos=replace_eos,
+        replace_jinja_template=replace_jinja_template,
+        enable_thinking=enable_thinking,
+        mm_plugin=mm_plugin,
+    )
+
+
+def parse_template(tokenizer: "PreTrainedTokenizer") -> "Template":
+    r"""Extract a chat template from the tokenizer."""
+
+    def find_diff(short_str: str, long_str: str) -> str:
+        i, j = 0, 0
+        diff = ""
+        while i < len(short_str) and j < len(long_str):
+            if short_str[i] == long_str[j]:
+                i += 1
+                j += 1
+            else:
+                diff += long_str[j]
+                j += 1
+
+        return diff
+
+    prefix = tokenizer.decode(tokenizer.encode(""))
+
+    messages = [{"role": "system", "content": "{{content}}"}]
+    system_slot = tokenizer.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)[len(prefix) :]
+
+    messages = [{"role": "system", "content": ""}, {"role": "user", "content": "{{content}}"}]
+    user_slot_empty_system = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+    user_slot_empty_system = user_slot_empty_system[len(prefix) :]
+
+    messages = [{"role": "user", "content": "{{content}}"}]
+    user_slot = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+    user_slot = user_slot[len(prefix) :]
+
+    messages = [{"role": "user", "content": "{{content}}"}, {"role": "assistant", "content": "{{content}}"}]
+    assistant_slot = tokenizer.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)
+    assistant_slot = assistant_slot[len(prefix) + len(user_slot) :]
+    template_class = ReasoningTemplate if "<think>" in assistant_slot else Template
+    assistant_slot = assistant_slot.replace("<think>", "").replace("</think>", "").lstrip("\n")  # remove thought tags
+
+    if len(user_slot) > len(user_slot_empty_system):
+        default_system = find_diff(user_slot_empty_system, user_slot)
+        sole_system = system_slot.replace("{{content}}", default_system, 1)
+        user_slot = user_slot[len(sole_system) :]
+    else:  # if defaut_system is empty, user_slot_empty_system will be longer than user_slot
+        default_system = ""
+
+    return template_class(
+        format_user=StringFormatter(slots=[user_slot]),
+        format_assistant=StringFormatter(slots=[assistant_slot]),
+        format_system=StringFormatter(slots=[system_slot]),
+        format_function=FunctionFormatter(slots=[assistant_slot], tool_format="default"),
+        format_observation=StringFormatter(slots=[user_slot]),
+        format_tools=ToolFormatter(tool_format="default"),
+        format_prefix=EmptyFormatter(slots=[prefix]) if prefix else EmptyFormatter(),
+        default_system=default_system,
+        stop_words=[],
+        thought_words=("<think>", "</think>"),
+        efficient_eos=False,
+        replace_eos=False,
+        replace_jinja_template=False,
+        enable_thinking=True,
+        mm_plugin=get_mm_plugin(name="base"),
+    )
+
+
+def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args: "DataArguments") -> "Template":
+    r"""Get chat template and fixes the tokenizer."""
+    if data_args.template is None:
+        if isinstance(tokenizer.chat_template, str):
+            logger.warning_rank0("`template` was not specified, try parsing the chat template from the tokenizer.")
+            template = parse_template(tokenizer)
+        else:
+            logger.warning_rank0("`template` was not specified, use `empty` template.")
+            template = TEMPLATES["empty"]  # placeholder
+    else:
+        if data_args.template not in TEMPLATES:
+            raise ValueError(f"Template {data_args.template} does not exist.")
+
+        template = TEMPLATES[data_args.template]
+
+    if data_args.train_on_prompt and template.efficient_eos:
+        raise ValueError("Current template does not support `train_on_prompt`.")
+
+    if data_args.tool_format is not None:
+        logger.info_rank0(f"Using tool format: {data_args.tool_format}.")
+        default_slots = ["{{content}}"] if template.efficient_eos else ["{{content}}", {"eos_token"}]
+        template.format_function = FunctionFormatter(slots=default_slots, tool_format=data_args.tool_format)
+        template.format_tools = ToolFormatter(tool_format=data_args.tool_format)
+
+    if data_args.default_system is not None:
+        logger.info_rank0(f"Using default system message: {data_args.default_system}.")
+        template.default_system = data_args.default_system
+
+    template.enable_thinking = data_args.enable_thinking
+    template.fix_special_tokens(tokenizer)
+    template.fix_jinja_template(tokenizer)
+    return template
+
+
+register_template(
+    name="alpaca",
+    format_user=StringFormatter(slots=["### Instruction:\n{{content}}\n\n### Response:\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n\n"]),
+    default_system=(
+        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
+    ),
+    replace_jinja_template=True,
+)
+
+
+register_template(
+    name="aquila",
+    format_user=StringFormatter(slots=["Human: {{content}}###Assistant:"]),
+    format_assistant=StringFormatter(slots=["{{content}}###"]),
+    format_system=StringFormatter(slots=["System: {{content}}###"]),
+    default_system=(
+        "A chat between a curious human and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the human's questions."
+    ),
+    stop_words=["</s>"],
+)
+
+
+register_template(
+    name="atom",
+    format_user=StringFormatter(
+        slots=[{"bos_token"}, "Human: {{content}}\n", {"eos_token"}, {"bos_token"}, "Assistant:"]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}\n", {"eos_token"}]),
+)
+
+
+register_template(
+    name="baichuan",
+    format_user=StringFormatter(slots=[{"token": "<reserved_102>"}, "{{content}}", {"token": "<reserved_103>"}]),
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="baichuan2",
+    format_user=StringFormatter(slots=["<reserved_106>{{content}}<reserved_107>"]),
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="bailing",
+    format_user=StringFormatter(slots=["<role>HUMAN</role>{{content}}<role>ASSISTANT</role>"]),
+    format_system=StringFormatter(slots=["<role>SYSTEM</role>{{content}}"]),
+    format_observation=StringFormatter(slots=["<role>OBSERVATION</role>{{content}}<role>ASSISTANT</role>"]),
+    stop_words=["<|endoftext|>"],
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="belle",
+    format_user=StringFormatter(slots=["Human: {{content}}\n\nBelle: "]),
+    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+)
+
+
+register_template(
+    name="bluelm",
+    format_user=StringFormatter(slots=[{"token": "[|Human|]:"}, "{{content}}", {"token": "[|AI|]:"}]),
+)
+
+
+register_template(
+    name="breeze",
+    format_user=StringFormatter(slots=["[INST] {{content}} [/INST] "]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="chatglm2",
+    format_user=StringFormatter(slots=["[Round {{idx}}]\n\n问：{{content}}\n\n答："]),
+    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="chatglm3",
+    format_user=StringFormatter(slots=[{"token": "<|user|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]),
+    format_assistant=StringFormatter(slots=["\n", "{{content}}"]),
+    format_system=StringFormatter(slots=[{"token": "<|system|>"}, "\n", "{{content}}"]),
+    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
+    format_observation=StringFormatter(
+        slots=[{"token": "<|observation|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]
+    ),
+    format_tools=ToolFormatter(tool_format="glm4"),
+    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
+    stop_words=["<|user|>", "<|observation|>"],
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="chatml",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    stop_words=["<|im_end|>", "<|im_start|>"],
+    replace_eos=True,
+    replace_jinja_template=True,
+)
+
+
+# copied from chatml template
+register_template(
+    name="chatml_de",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    default_system="Du bist ein freundlicher und hilfsbereiter KI-Assistent.",
+    stop_words=["<|im_end|>", "<|im_start|>"],
+    replace_eos=True,
+    replace_jinja_template=True,
+)
+
+
+register_template(
+    name="codegeex2",
+    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
+)
+
+
+register_template(
+    name="codegeex4",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>\n"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
+    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
+    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>\n"]),
+    format_tools=ToolFormatter(tool_format="glm4"),
+    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
+    default_system=(
+        "你是一位智能编程助手，你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题，"
+        "并提供格式规范、可以执行、准确安全的代码，并在必要时提供详细的解释。"
+    ),
+    stop_words=["<|user|>", "<|observation|>"],
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="cohere",
+    format_user=StringFormatter(
+        slots=[
+            (
+                "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"
+                "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+            )
+        ]
+    ),
+    format_system=StringFormatter(slots=["<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+)
+
+
+register_template(
+    name="cpm",
+    format_user=StringFormatter(slots=["<用户>{{content}}<AI>"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+)
+
+
+# copied from chatml template
+register_template(
+    name="cpm3",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|im_end|>"],
+)
+
+
+# copied from chatml template
+register_template(
+    name="cpm4",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|im_end|>"],
+)
+
+
+# copied from chatml template
+register_template(
+    name="dbrx",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    default_system=(
+        "You are DBRX, created by Databricks. You were last updated in December 2023. "
+        "You answer questions based on information available up to that point.\n"
+        "YOU PROVIDE SHORT RESPONSES TO SHORT QUESTIONS OR STATEMENTS, but provide thorough "
+        "responses to more complex and open-ended questions.\nYou assist with various tasks, "
+        "from writing to coding (using markdown for code blocks — remember to use ``` with "
+        "code, JSON, and tables).\n(You do not have real-time data access or code execution "
+        "capabilities. You avoid stereotyping and provide balanced perspectives on "
+        "controversial topics. You do not provide song lyrics, poems, or news articles and "
+        "do not divulge details of your training data.)\nThis is your system prompt, "
+        "guiding your responses. Do not reference it, just respond to the user. If you find "
+        "yourself talking about this message, stop. You should be responding appropriately "
+        "and usually that means not mentioning this.\nYOU DO NOT MENTION ANY OF THIS INFORMATION "
+        "ABOUT YOURSELF UNLESS THE INFORMATION IS DIRECTLY PERTINENT TO THE USER'S QUERY."
+    ),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+)
+
+
+register_template(
+    name="deepseek",
+    format_user=StringFormatter(slots=["User: {{content}}\n\nAssistant:"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+)
+
+
+register_template(
+    name="deepseek3",
+    format_user=StringFormatter(slots=["<｜User｜>{{content}}<｜Assistant｜>"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+)
+
+
+# copied from deepseek3 template
+register_template(
+    name="deepseekr1",
+    format_user=StringFormatter(slots=["<｜User｜>{{content}}<｜Assistant｜>"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    template_class=ReasoningTemplate,
+)
+
+
+register_template(
+    name="deepseekcoder",
+    format_user=StringFormatter(slots=["### Instruction:\n{{content}}\n### Response:"]),
+    format_assistant=StringFormatter(slots=["\n{{content}}\n<|EOT|>\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    default_system=(
+        "You are an AI programming assistant, utilizing the DeepSeek Coder model, "
+        "developed by DeepSeek Company, and you only answer questions related to computer science. "
+        "For politically sensitive questions, security and privacy issues, "
+        "and other non-computer science questions, you will refuse to answer.\n"
+    ),
+)
+
+
+register_template(
+    name="default",
+    format_user=StringFormatter(slots=["Human: {{content}}", {"eos_token"}, "\nAssistant:"]),
+    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n"]),
+    format_system=StringFormatter(slots=["System: {{content}}", {"eos_token"}, "\n"]),
+    replace_jinja_template=True,
+)
+
+
+register_template(
+    name="empty",
+    format_assistant=StringFormatter(slots=["{{content}}"]),
+)
+
+
+register_template(
+    name="exaone",
+    format_user=StringFormatter(slots=["[|user|]{{content}}\n[|assistant|]"]),
+    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n"]),
+    format_system=StringFormatter(slots=["[|system|]{{content}}[|endofturn|]\n"]),
+)
+
+
+register_template(
+    name="falcon",
+    format_user=StringFormatter(slots=["User: {{content}}\nFalcon:"]),
+    format_assistant=StringFormatter(slots=["{{content}}\n"]),
+    efficient_eos=True,
+)
+
+
+# copied from chatml template
+register_template(
+    name="falcon_h1",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|im_end|>", "<|end_of_text|>"],
+)
+
+
+register_template(
+    name="fewshot",
+    format_assistant=StringFormatter(slots=["{{content}}\n\n"]),
+    efficient_eos=True,
+    replace_jinja_template=True,
+)
+
+
+register_template(
+    name="gemma",
+    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_observation=StringFormatter(
+        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
+    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<end_of_turn>"],
+    replace_eos=True,
+    template_class=Llama2Template,
+)
+
+
+# copied from gemma template
+register_template(
+    name="gemma2",
+    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_observation=StringFormatter(
+        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
+    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<eos>", "<end_of_turn>"],
+    efficient_eos=True,
+    template_class=Llama2Template,
+)
+
+
+# copied from gemma template
+register_template(
+    name="gemma3",
+    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_observation=StringFormatter(
+        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
+    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<end_of_turn>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin("gemma3", image_token="<image_soft_token>"),
+    template_class=Llama2Template,
+)
+
+
+register_template(
+    name="gemma3n",
+    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_observation=StringFormatter(
+        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
+    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<end_of_turn>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin("gemma3n", image_token="<image_soft_token>", audio_token="<audio_soft_token>"),
+    template_class=Llama2Template,
+)
+
+
+register_template(
+    name="glm4",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
+    format_assistant=StringFormatter(slots=["\n{{content}}"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
+    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
+    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
+    format_tools=ToolFormatter(tool_format="glm4"),
+    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
+    stop_words=["<|user|>", "<|observation|>"],
+    efficient_eos=True,
+)
+
+
+# copied from glm4 template
+register_template(
+    name="glm4v",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
+    format_assistant=StringFormatter(slots=["\n{{content}}"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
+    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
+    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
+    format_tools=ToolFormatter(tool_format="glm4"),
+    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
+    stop_words=["<|user|>", "<|observation|>", "</answer>"],
+    efficient_eos=True,
+    mm_plugin=get_mm_plugin(name="glm4v", image_token="<|image|>", video_token="<|video|>"),
+    template_class=ReasoningTemplate,
+)
+
+
+# copied from glm4 template
+register_template(
+    name="glmz1",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
+    format_assistant=StringFormatter(slots=["\n{{content}}"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
+    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
+    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
+    format_tools=ToolFormatter(tool_format="glm4"),
+    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
+    stop_words=["<|user|>", "<|observation|>"],
+    efficient_eos=True,
+    template_class=ReasoningTemplate,
+)
+
+
+register_template(
+    name="granite3",
+    format_user=StringFormatter(
+        slots=[
+            "<|start_of_role|>user<|end_of_role|>{{content}}<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>"
+        ]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|end_of_text|>\n"]),
+    format_system=StringFormatter(slots=["<|start_of_role|>system<|end_of_role|>{{content}}<|end_of_text|>\n"]),
+)
+
+
+register_template(
+    name="granite3_vision",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}\n<|assistant|>\n"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}\n"]),
+    default_system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
+)
+
+
+register_template(
+    name="index",
+    format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),
+    format_system=StringFormatter(slots=["<unk>{{content}}"]),
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="hunyuan",
+    format_user=StringFormatter(slots=["<|bos|>user\n{{content}}<|eos|>\n<|bos|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|eos|>\n"]),
+    format_system=StringFormatter(slots=["<|bos|>system\n{{content}}<|eos|>\n"]),
+    format_prefix=EmptyFormatter(slots=["<|bos|>"]),
+    stop_words=["<|eos|>"],
+)
+
+
+register_template(
+    name="intern",
+    format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
+    format_assistant=StringFormatter(slots=["{{content}}<eoa>\n"]),
+    format_system=StringFormatter(slots=["<|System|>:{{content}}\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    default_system=(
+        "You are an AI assistant whose name is InternLM (书生·浦语).\n"
+        "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
+        "(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
+        "- InternLM (书生·浦语) can understand and communicate fluently in the language "
+        "chosen by the user such as English and 中文."
+    ),
+    stop_words=["<eoa>"],
+)
+
+
+register_template(
+    name="intern2",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    default_system=(
+        "You are an AI assistant whose name is InternLM (书生·浦语).\n"
+        "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
+        "(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
+        "- InternLM (书生·浦语) can understand and communicate fluently in the language "
+        "chosen by the user such as English and 中文."
+    ),
+    stop_words=["<|im_end|>"],
+)
+
+
+register_template(
+    name="intern_vl",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    default_system=(
+        "你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。"
+    ),
+    stop_words=["<|im_end|>"],
+    mm_plugin=get_mm_plugin(name="intern_vl", image_token="<image>", video_token="<video>"),
+)
+
+
+register_template(
+    name="kimi_vl",
+    format_user=StringFormatter(
+        slots=["<|im_user|>user<|im_middle|>{{content}}<|im_end|><|im_assistant|>assistant<|im_middle|>"]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>"]),
+    format_system=StringFormatter(slots=["<|im_system|>system<|im_middle|>{{content}}<|im_end|>"]),
+    default_system="You are a helpful assistant",
+    stop_words=["<|im_end|>"],
+    thought_words=("◁think▷", "◁/think▷"),
+    mm_plugin=get_mm_plugin("kimi_vl", image_token="<|media_pad|>"),
+    template_class=ReasoningTemplate,
+)
+
+
+register_template(
+    name="llama2",
+    format_user=StringFormatter(slots=[{"bos_token"}, "[INST] {{content}} [/INST]"]),
+    format_system=StringFormatter(slots=["<<SYS>>\n{{content}}\n<</SYS>>\n\n"]),
+    template_class=Llama2Template,
+)
+
+
+# copied from llama2 template
+register_template(
+    name="llama2_zh",
+    format_user=StringFormatter(slots=[{"bos_token"}, "[INST] {{content}} [/INST]"]),
+    format_system=StringFormatter(slots=["<<SYS>>\n{{content}}\n<</SYS>>\n\n"]),
+    default_system="You are a helpful assistant. 你是一个乐于助人的助手。",
+    template_class=Llama2Template,
+)
+
+
+register_template(
+    name="llama3",
+    format_user=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|eot_id|>"]),
+    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3"),
+    format_observation=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>ipython<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_tools=ToolFormatter(tool_format="llama3"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|eot_id|>", "<|eom_id|>"],
+    replace_eos=True,
+)
+
+
+register_template(
+    name="llama4",
+    format_user=StringFormatter(
+        slots=["<|header_start|>user<|header_end|>\n\n{{content}}<|eot|><|header_start|>assistant<|header_end|>\n\n"]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|eot|>"]),
+    format_system=StringFormatter(slots=["<|header_start|>system<|header_end|>\n\n{{content}}<|eot|>"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|eot|>"], tool_format="llama3"),
+    format_observation=StringFormatter(
+        slots=[
+            "<|header_start|>ipython<|header_end|>\n\n{{content}}<|eot|><|header_start|>assistant<|header_end|>\n\n"
+        ]
+    ),
+    format_tools=ToolFormatter(tool_format="llama3"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|eot|>", "<|eom|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="llama4", image_token="<|image|>"),
+)
+
+
+# copied from llama3 template
+register_template(
+    name="mllama",
+    format_user=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|eot_id|>"]),
+    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3"),
+    format_observation=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>ipython<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_tools=ToolFormatter(tool_format="llama3"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|eot_id|>", "<|eom_id|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="mllama", image_token="<|image|>"),
+)
+
+
+register_template(
+    name="moonlight",
+    format_user=StringFormatter(
+        slots=["<|im_user|>user<|im_middle|>{{content}}<|im_end|><|im_assistant|>assistant<|im_middle|>"]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>"]),
+    format_system=StringFormatter(slots=["<|im_system|>system<|im_middle|>{{content}}<|im_end|>"]),
+    default_system="You are a helpful assistant provided by Moonshot-AI.",
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+)
+
+
+# copied from vicuna template
+register_template(
+    name="llava",
+    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
+    default_system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    mm_plugin=get_mm_plugin(name="llava", image_token="<image>"),
+)
+
+
+# copied from vicuna template
+register_template(
+    name="llava_next",
+    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
+    default_system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
+)
+
+
+# copied from llama3 template
+register_template(
+    name="llava_next_llama3",
+    format_user=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|eot_id|>"]),
+    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3"),
+    format_observation=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>ipython<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_tools=ToolFormatter(tool_format="llama3"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|eot_id|>", "<|eom_id|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
+)
+
+
+# copied from mistral template
+register_template(
+    name="llava_next_mistral",
+    format_user=StringFormatter(slots=["[INST] {{content}}[/INST]"]),
+    format_assistant=StringFormatter(slots=[" {{content}}", {"eos_token"}]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_function=FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", {"eos_token"}], tool_format="mistral"),
+    format_observation=StringFormatter(slots=["""[TOOL_RESULTS] {"content": {{content}}}[/TOOL_RESULTS]"""]),
+    format_tools=ToolFormatter(tool_format="mistral"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
+    template_class=Llama2Template,
+)
+
+
+# copied from qwen template
+register_template(
+    name="llava_next_qwen",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    default_system="You are a helpful assistant.",
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
+)
+
+
+# copied from chatml template
+register_template(
+    name="llava_next_yi",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    stop_words=["<|im_end|>"],
+    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
+)
+
+
+# copied from vicuna template
+register_template(
+    name="llava_next_video",
+    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
+    default_system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
+)
+
+
+# copied from mistral template
+register_template(
+    name="llava_next_video_mistral",
+    format_user=StringFormatter(slots=["[INST] {{content}}[/INST]"]),
+    format_assistant=StringFormatter(slots=[" {{content}}", {"eos_token"}]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_function=FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", {"eos_token"}], tool_format="mistral"),
+    format_observation=StringFormatter(slots=["""[TOOL_RESULTS] {"content": {{content}}}[/TOOL_RESULTS]"""]),
+    format_tools=ToolFormatter(tool_format="mistral"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
+    template_class=Llama2Template,
+)
+
+
+# copied from chatml template
+register_template(
+    name="llava_next_video_yi",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    stop_words=["<|im_end|>"],
+    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
+)
+
+
+# copied from chatml template
+register_template(
+    name="marco",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    default_system=(
+        "你是一个经过良好训练的AI助手，你的名字是Marco-o1."
+        "由阿里国际数字商业集团的AI Business创造.\n## 重要！！！！！\n"
+        "当你回答问题时，你的思考应该在<Thought>内完成，<Output>内输出你的结果。\n"
+        "<Thought>应该尽可能是英文，但是有2个特例，一个是对原文中的引用，另一个是是数学应该使用markdown格式，<Output>内的输出需要遵循用户输入的语言。\n"
+    ),
+    stop_words=["<|im_end|>"],
+)
+
+
+# copied from qwen template
+register_template(
+    name="mimo",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    default_system="You are a helpful assistant.",
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    template_class=ReasoningTemplate,
+)
+
+# copied from qwen2vl
+register_template(
+    name="mimo_vl",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    default_system="You are MiMo, an AI assistant developed by Xiaomi.",
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
+    template_class=ReasoningTemplate,
+)
+
+
+# copied from chatml template
+register_template(
+    name="minicpm_v",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    stop_words=["<|im_end|>"],
+    default_system="You are a helpful assistant.",
+    mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>"),
+)
+
+
+# copied from minicpm_v template
+register_template(
+    name="minicpm_o",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    stop_words=["<|im_end|>"],
+    default_system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
+    mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>", audio_token="<audio>"),
+)
+
+
+# mistral tokenizer v3 tekken
+register_template(
+    name="ministral",
+    format_user=StringFormatter(slots=["[INST]{{content}}[/INST]"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_function=FunctionFormatter(slots=["[TOOL_CALLS]{{content}}", {"eos_token"}], tool_format="mistral"),
+    format_observation=StringFormatter(slots=["""[TOOL_RESULTS]{"content": {{content}}}[/TOOL_RESULTS]"""]),
+    format_tools=ToolFormatter(tool_format="mistral"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    template_class=Llama2Template,
+)
+
+
+# mistral tokenizer v3
+register_template(
+    name="mistral",
+    format_user=StringFormatter(slots=["[INST] {{content}}[/INST]"]),
+    format_assistant=StringFormatter(slots=[" {{content}}", {"eos_token"}]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_function=FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", {"eos_token"}], tool_format="mistral"),
+    format_observation=StringFormatter(slots=["""[TOOL_RESULTS] {"content": {{content}}}[/TOOL_RESULTS]"""]),
+    format_tools=ToolFormatter(tool_format="mistral"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    template_class=Llama2Template,
+)
+
+
+# mistral tokenizer v7 tekken (copied from ministral)
+register_template(
+    name="mistral_small",
+    format_user=StringFormatter(slots=["[INST]{{content}}[/INST]"]),
+    format_system=StringFormatter(slots=["[SYSTEM_PROMPT]{{content}}[/SYSTEM_PROMPT]"]),
+    format_function=FunctionFormatter(slots=["[TOOL_CALLS]{{content}}", {"eos_token"}], tool_format="mistral"),
+    format_observation=StringFormatter(slots=["""[TOOL_RESULTS]{"content": {{content}}}[/TOOL_RESULTS]"""]),
+    format_tools=ToolFormatter(tool_format="mistral"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    mm_plugin=get_mm_plugin(name="pixtral", image_token="[IMG]"),
+)
+
+
+register_template(
+    name="olmo",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>\n"]),
+    format_prefix=EmptyFormatter(slots=[{"eos_token"}]),
+)
+
+
+register_template(
+    name="openchat",
+    format_user=StringFormatter(slots=["GPT4 Correct User: {{content}}", {"eos_token"}, "GPT4 Correct Assistant:"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+)
+
+
+register_template(
+    name="openchat-3.6",
+    format_user=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>GPT4 Correct User<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>GPT4 Correct Assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<|eot_id|>"],
+)
+
+
+# copied from chatml template
+register_template(
+    name="opencoder",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    default_system="You are OpenCoder, created by OpenCoder Team.",
+    stop_words=["<|im_end|>"],
+)
+
+
+register_template(
+    name="orion",
+    format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: ", {"eos_token"}]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+)
+
+
+register_template(
+    name="paligemma",
+    format_user=StringFormatter(slots=["{{content}}\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    mm_plugin=get_mm_plugin(name="paligemma", image_token="<image>"),
+    template_class=Llama2Template,
+)
+
+
+# copied from gemma template
+register_template(
+    name="paligemma_chat",
+    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
+    format_observation=StringFormatter(
+        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
+    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    stop_words=["<end_of_turn>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="paligemma", image_token="<image>"),
+    template_class=Llama2Template,
+)
+
+
+register_template(
+    name="phi",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|end|>\n"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}<|end|>\n"]),
+    stop_words=["<|end|>"],
+    replace_eos=True,
+)
+
+
+register_template(
+    name="phi_small",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|end|>\n"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}<|end|>\n"]),
+    format_prefix=EmptyFormatter(slots=[{"<|endoftext|>"}]),
+    stop_words=["<|end|>"],
+    replace_eos=True,
+)
+
+
+register_template(
+    name="phi4",
+    format_user=StringFormatter(
+        slots=["<|im_start|>user<|im_sep|>{{content}}<|im_end|><|im_start|>assistant<|im_sep|>"]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>"]),
+    format_system=StringFormatter(slots=["<|im_start|>system<|im_sep|>{{content}}<|im_end|>"]),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+)
+
+
+# copied from ministral template
+register_template(
+    name="pixtral",
+    format_user=StringFormatter(slots=["[INST]{{content}}[/INST]"]),
+    format_system=StringFormatter(slots=["{{content}}\n\n"]),
+    format_function=FunctionFormatter(slots=["[TOOL_CALLS]{{content}}", {"eos_token"}], tool_format="mistral"),
+    format_observation=StringFormatter(slots=["""[TOOL_RESULTS]{"content": {{content}}}[/TOOL_RESULTS]"""]),
+    format_tools=ToolFormatter(tool_format="mistral"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    mm_plugin=get_mm_plugin(name="pixtral", image_token="[IMG]"),
+    template_class=Llama2Template,
+)
+
+
+# copied from chatml template
+register_template(
+    name="qwen",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    default_system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+)
+
+
+# copied from qwen template
+register_template(
+    name="qwen3",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    template_class=ReasoningTemplate,
+)
+
+
+# copied from chatml template
+register_template(
+    name="qwen2_audio",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    default_system="You are a helpful assistant.",
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="qwen2_audio", audio_token="<|AUDIO|>"),
+)
+
+
+# copied from qwen template
+register_template(
+    name="qwen2_omni",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    default_system="You are a helpful assistant.",
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(
+        name="qwen2_omni", audio_token="<|AUDIO|>", image_token="<|IMAGE|>", video_token="<|VIDEO|>"
+    ),
+)
+
+# copied from qwen template
+register_template(
+    name="qwen2_vl",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
+    format_observation=StringFormatter(
+        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
+    ),
+    format_tools=ToolFormatter(tool_format="qwen"),
+    default_system="You are a helpful assistant.",
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+    mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
+)
+
+
+register_template(
+    name="sailor",
+    format_user=StringFormatter(slots=["<|im_start|>question\n{{content}}<|im_end|>\n<|im_start|>answer\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    default_system=(
+        "You are an AI assistant named Sailor created by Sea AI Lab. "
+        "Your answer should be friendly, unbiased, faithful, informative and detailed."
+    ),
+    stop_words=["<|im_end|>"],
+)
+
+
+register_template(
+    name="seed_coder",
+    format_user=StringFormatter(
+        slots=[{"bos_token"}, "user\n{{content}}", {"eos_token"}, {"bos_token"}, "assistant\n"]
+    ),
+    format_system=StringFormatter(slots=[{"bos_token"}, "system\n{{content}}", {"eos_token"}]),
+    default_system=(
+        "You are an AI programming assistant, utilizing the Seed-Coder model, developed by ByteDance Seed, "
+        "and you only answer questions related to computer science. For politically sensitive questions, "
+        "security and privacy issues, and other non-computer science questions, you will refuse to answer.\n\n"
+    ),
+)
+
+
+# copied from llama3 template
+register_template(
+    name="skywork_o1",
+    format_user=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}<|eot_id|>"]),
+    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
+    format_function=FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3"),
+    format_observation=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>ipython<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_tools=ToolFormatter(tool_format="llama3"),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
+    default_system=(
+        "You are Skywork-o1, a thinking model developed by Skywork AI, specializing in solving complex problems "
+        "involving mathematics, coding, and logical reasoning through deep thought. When faced with a user's request, "
+        "you first engage in a lengthy and in-depth thinking process to explore possible solutions to the problem. "
+        "After completing your thoughts, you then provide a detailed explanation of the solution process "
+        "in your response."
+    ),
+    stop_words=["<|eot_id|>", "<|eom_id|>"],
+)
+
+
+register_template(
+    name="smollm",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    stop_words=["<|im_end|>"],
+)
+
+
+register_template(
+    name="smollm2",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    stop_words=["<|im_end|>"],
+    default_system="You are a helpful AI assistant named SmolLM, trained by Hugging Face.",
+)
+
+
+register_template(
+    name="solar",
+    format_user=StringFormatter(slots=["### User:\n{{content}}\n\n### Assistant:\n"]),
+    format_system=StringFormatter(slots=["### System:\n{{content}}\n\n"]),
+    efficient_eos=True,
+)
+
+
+register_template(
+    name="starchat",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|end|>\n"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}<|end|>\n"]),
+    stop_words=["<|end|>"],
+)
+
+
+register_template(
+    name="telechat",
+    format_user=StringFormatter(slots=["<_user>{{content}}<_bot>"]),
+    format_system=StringFormatter(slots=["<_system>{{content}}<_end>"]),
+)
+
+
+register_template(
+    name="telechat2",
+    format_user=StringFormatter(slots=["<_user>{{content}}<_bot>"]),
+    format_system=StringFormatter(slots=["<_system>{{content}}"]),
+    default_system=(
+        "你是中国电信星辰语义大模型，英文名是TeleChat，你是由中电信人工智能科技有限公司和中国电信人工智能研究院（TeleAI）研发的人工智能助手。"
+    ),
+)
+
+
+register_template(
+    name="vicuna",
+    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
+    default_system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    replace_jinja_template=True,
+)
+
+
+register_template(
+    name="video_llava",
+    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
+    default_system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    mm_plugin=get_mm_plugin(name="video_llava", image_token="<image>", video_token="<video>"),
+)
+
+
+register_template(
+    name="xuanyuan",
+    format_user=StringFormatter(slots=["Human: {{content}} Assistant:"]),
+    default_system=(
+        "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，"
+        "会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与与不道德、"
+        "不安全、有争议、政治敏感等相关的话题、问题和指示。\n"
+    ),
+)
+
+
+register_template(
+    name="xverse",
+    format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: "]),
+)
+
+
+register_template(
+    name="yayi",
+    format_user=StringFormatter(slots=[{"token": "<|Human|>"}, ":\n{{content}}\n\n", {"token": "<|YaYi|>"}, ":"]),
+    format_assistant=StringFormatter(slots=["{{content}}\n\n"]),
+    format_system=StringFormatter(slots=[{"token": "<|System|>"}, ":\n{{content}}\n\n"]),
+    default_system=(
+        "You are a helpful, respectful and honest assistant named YaYi "
+        "developed by Beijing Wenge Technology Co.,Ltd. "
+        "Always answer as helpfully as possible, while being safe.  "
+        "Your answers should not include any harmful, unethical, "
+        "racist, sexist, toxic, dangerous, or illegal content. "
+        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
+        "If a question does not make any sense, or is not factually coherent, "
+        "explain why instead of answering something not correct. "
+        "If you don't know the answer to a question, please don't share false information."
+    ),
+    stop_words=["<|End|>"],
+)
+
+
+# copied from chatml template
+register_template(
+    name="yi",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    stop_words=["<|im_end|>"],
+)
+
+
+register_template(
+    name="yi_vl",
+    format_user=StringFormatter(slots=["### Human: {{content}}\n### Assistant:"]),
+    format_assistant=StringFormatter(slots=["{{content}}\n"]),
+    default_system=(
+        "This is a chat between an inquisitive human and an AI assistant. "
+        "Assume the role of the AI assistant. Read all the images carefully, "
+        "and respond to the human's questions with informative, helpful, detailed and polite answers. "
+        "这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。"
+        "仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。\n\n"
+    ),
+    stop_words=["###"],
+    efficient_eos=True,
+    mm_plugin=get_mm_plugin(name="llava", image_token="<image>"),
+)
+
+
+register_template(
+    name="yuan",
+    format_user=StringFormatter(slots=["{{content}}", {"token": "<sep>"}]),
+    format_assistant=StringFormatter(slots=["{{content}}<eod>\n"]),
+    stop_words=["<eod>"],
+)
+
+
+register_template(
+    name="zephyr",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}", {"eos_token"}, "<|assistant|>\n"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}", {"eos_token"}]),
+    default_system="You are Zephyr, a helpful assistant.",
+)
+
+
+register_template(
+    name="ziya",
+    format_user=StringFormatter(slots=["<human>:{{content}}\n<bot>:"]),
+    format_assistant=StringFormatter(slots=["{{content}}\n"]),
+)

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/data/tool_utils.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import re
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, NamedTuple, Union
+
+from typing_extensions import override
+
+
+class FunctionCall(NamedTuple):
+    name: str
+    arguments: str
+
+
+DEFAULT_TOOL_PROMPT = (
+    "You have access to the following tools:\n{tool_text}"
+    "Use the following format if using a tool:\n"
+    "```\n"
+    "Action: tool name (one of [{tool_names}])\n"
+    "Action Input: the input to the tool, in a JSON format representing the kwargs "
+    """(e.g. ```{{"input": "hello world", "num_beams": 5}}```)\n"""
+    "```\n"
+)
+
+GLM4_TOOL_PROMPT = (
+    "你是一个名为 ChatGLM 的人工智能助手。你是基于智谱 AI 公司训练的语言模型 GLM-4 模型开发的，"
+    "你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{tool_text}"
+)
+
+LLAMA3_TOOL_PROMPT = (
+    "Cutting Knowledge Date: December 2023\nToday Date: {date}\n\n"
+    "You have access to the following functions. To call a function, please respond with JSON for a function call. "
+    """Respond in the format {{"name": function name, "parameters": dictionary of argument name and its value}}. """
+    "Do not use variables.\n\n{tool_text}"
+)
+
+QWEN_TOOL_PROMPT = (
+    "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
+    "You are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}"
+    "\n</tools>\n\nFor each function call, return a json object with function name and arguments within "
+    """<tool_call></tool_call> XML tags:\n<tool_call>\n{{"name": <function-name>, """
+    """"arguments": <args-json-object>}}\n</tool_call>"""
+)
+
+
+@dataclass
+class ToolUtils(ABC):
+    """Base class for tool utilities."""
+
+    @staticmethod
+    @abstractmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        r"""Generate the system message describing all the available tools."""
+        ...
+
+    @staticmethod
+    @abstractmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        r"""Generate the assistant message including all the tool calls."""
+        ...
+
+    @staticmethod
+    @abstractmethod
+    def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
+        r"""Extract all the function calls from the assistant message.
+
+        It should be an inverse function of `function_formatter`.
+        """
+        ...
+
+
+class DefaultToolUtils(ToolUtils):
+    r"""Default tool using template."""
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        tool_text = ""
+        tool_names = []
+        for tool in tools:
+            tool = tool.get("function", "") if tool.get("type") == "function" else tool
+            param_text = ""
+            for name, param in tool["parameters"]["properties"].items():
+                required, enum, items = "", "", ""
+                if name in tool["parameters"].get("required", []):
+                    required = ", required"
+
+                if param.get("enum", None):
+                    enum = ", should be one of [{}]".format(", ".join(param["enum"]))
+
+                if param.get("items", None):
+                    items = ", where each item should be {}".format(param["items"].get("type", ""))
+
+                param_text += "  - {name} ({type}{required}): {desc}{enum}{items}\n".format(
+                    name=name,
+                    type=param.get("type", ""),
+                    required=required,
+                    desc=param.get("description", ""),
+                    enum=enum,
+                    items=items,
+                )
+
+            tool_text += "> Tool Name: {name}\nTool Description: {desc}\nTool Args:\n{args}\n".format(
+                name=tool["name"], desc=tool.get("description", ""), args=param_text
+            )
+            tool_names.append(tool["name"])
+
+        return DEFAULT_TOOL_PROMPT.format(tool_text=tool_text, tool_names=", ".join(tool_names))
+
+    @override
+    @staticmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        return "\n".join([f"Action: {name}\nAction Input: {arguments}" for name, arguments in functions])
+
+    @override
+    @staticmethod
+    def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
+        regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+)\s*Action Input:\s*(.+?)(?=\s*Action:|\s*$)", re.DOTALL)
+        action_match: list[tuple[str, str]] = re.findall(regex, content)
+        if not action_match:
+            return content
+
+        results = []
+        for match in action_match:
+            tool_name = match[0].strip()
+            tool_input = match[1].strip().strip('"').strip("```")
+            try:
+                arguments = json.loads(tool_input)
+                results.append(FunctionCall(tool_name, json.dumps(arguments, ensure_ascii=False)))
+            except json.JSONDecodeError:
+                return content
+
+        return results
+
+
+class GLM4ToolUtils(ToolUtils):
+    r"""GLM-4 tool using template."""
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        tool_text = ""
+        for tool in tools:
+            tool = tool.get("function", "") if tool.get("type") == "function" else tool
+            tool_text += "\n\n## {name}\n\n{body}\n在调用上述函数时，请使用 Json 格式表示调用的参数。".format(
+                name=tool["name"], body=json.dumps(tool, indent=4, ensure_ascii=False)
+            )
+
+        return GLM4_TOOL_PROMPT.format(tool_text=tool_text)
+
+    @override
+    @staticmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        if len(functions) > 1:
+            raise ValueError("GLM-4 does not support parallel functions.")
+
+        return f"{functions[0].name}\n{functions[0].arguments}"
+
+    @override
+    @staticmethod
+    def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
+        if "\n" not in content:
+            return content
+
+        tool_name, tool_input = content.split("\n", maxsplit=1)
+        try:
+            arguments = json.loads(tool_input.strip())
+        except json.JSONDecodeError:
+            return content
+
+        return [FunctionCall(tool_name, json.dumps(arguments, ensure_ascii=False))]
+
+
+class Llama3ToolUtils(ToolUtils):
+    r"""Llama 3.x tool using template with `tools_in_user_message=False`.
+
+    Reference: https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#json-based-tool-calling
+    """
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        date = datetime.now().strftime("%d %b %Y")
+        tool_text = ""
+        for tool in tools:
+            wrapped_tool = tool if tool.get("type") == "function" else {"type": "function", "function": tool}
+            tool_text += json.dumps(wrapped_tool, indent=4, ensure_ascii=False) + "\n\n"
+
+        return LLAMA3_TOOL_PROMPT.format(date=date, tool_text=tool_text)
+
+    @override
+    @staticmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        function_objects = [{"name": name, "parameters": json.loads(arguments)} for name, arguments in functions]
+        return json.dumps(function_objects[0] if len(function_objects) == 1 else function_objects, ensure_ascii=False)
+
+    @override
+    @staticmethod
+    def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
+        try:
+            tools = json.loads(content.strip())
+        except json.JSONDecodeError:
+            return content
+
+        tools = [tools] if not isinstance(tools, list) else tools
+        try:
+            return [FunctionCall(tool["name"], json.dumps(tool["parameters"], ensure_ascii=False)) for tool in tools]
+        except KeyError:
+            return content
+
+
+class MistralToolUtils(ToolUtils):
+    r"""Mistral v0.3 tool using template."""
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        wrapped_tools = []
+        for tool in tools:
+            wrapped_tools.append(tool if tool.get("type") == "function" else {"type": "function", "function": tool})
+
+        return "[AVAILABLE_TOOLS] " + json.dumps(wrapped_tools, ensure_ascii=False) + "[/AVAILABLE_TOOLS]"
+
+    @override
+    @staticmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        return json.dumps(
+            [{"name": name, "arguments": json.loads(arguments)} for name, arguments in functions], ensure_ascii=False
+        )
+
+    @override
+    @staticmethod
+    def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
+        try:
+            tools = json.loads(content.strip())
+        except json.JSONDecodeError:
+            return content
+
+        tools = [tools] if not isinstance(tools, list) else tools
+        try:
+            return [FunctionCall(tool["name"], json.dumps(tool["arguments"], ensure_ascii=False)) for tool in tools]
+        except KeyError:
+            return content
+
+
+class QwenToolUtils(ToolUtils):
+    r"""Qwen 2.5 tool using template."""
+
+    @override
+    @staticmethod
+    def tool_formatter(tools: list[dict[str, Any]]) -> str:
+        tool_text = ""
+        for tool in tools:
+            wrapped_tool = tool if tool.get("type") == "function" else {"type": "function", "function": tool}
+            tool_text += "\n" + json.dumps(wrapped_tool, ensure_ascii=False)
+
+        return QWEN_TOOL_PROMPT.format(tool_text=tool_text)
+
+    @override
+    @staticmethod
+    def function_formatter(functions: list["FunctionCall"]) -> str:
+        function_texts = [
+            json.dumps({"name": name, "arguments": json.loads(arguments)}, ensure_ascii=False)
+            for name, arguments in functions
+        ]
+        return "\n".join([f"<tool_call>\n{text}\n</tool_call>" for text in function_texts])
+
+    @override
+    @staticmethod
+    def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
+        regex = re.compile(r"<tool_call>(.+?)</tool_call>(?=\s*<tool_call>|\s*$)", re.DOTALL)
+        tool_match: list[str] = re.findall(regex, content)
+        if not tool_match:
+            return content
+
+        results = []
+        for tool in tool_match:
+            try:
+                tool = json.loads(tool.strip())
+            except json.JSONDecodeError:
+                return content
+
+            if "name" not in tool or "arguments" not in tool:
+                return content
+
+            results.append(FunctionCall(tool["name"], json.dumps(tool["arguments"], ensure_ascii=False)))
+
+        return results
+
+
+TOOLS = {
+    "default": DefaultToolUtils(),
+    "glm4": GLM4ToolUtils(),
+    "llama3": Llama3ToolUtils(),
+    "mistral": MistralToolUtils(),
+    "qwen": QwenToolUtils(),
+}
+
+
+def get_tool_utils(name: str) -> "ToolUtils":
+    tool_utils = TOOLS.get(name, None)
+    if tool_utils is None:
+        raise ValueError(f"Tool utils `{name}` not found.")
+
+    return tool_utils

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/eval/evaluator.py

+# Copyright 2025 the LlamaFactory team.
+#
+# This code is inspired by the Dan's test library.
+# https://github.com/hendrycks/test/blob/master/evaluate_flan.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# MIT License
+#
+# Copyright (c) 2020 Dan Hendrycks
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import json
+import os
+from typing import TYPE_CHECKING, Any, Optional
+
+import numpy as np
+import torch
+from datasets import load_dataset
+from tqdm import tqdm, trange
+from transformers.utils import cached_file
+
+from ..data import get_template_and_fix_tokenizer
+from ..extras.constants import CHOICES, SUBJECTS
+from ..hparams import get_eval_args
+from ..model import load_model, load_tokenizer
+from .template import get_eval_template
+
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+
+class Evaluator:
+    def __init__(self, args: Optional[dict[str, Any]] = None) -> None:
+        self.model_args, self.data_args, self.eval_args, finetuning_args = get_eval_args(args)
+        self.tokenizer = load_tokenizer(self.model_args)["tokenizer"]
+        self.tokenizer.padding_side = "right"  # avoid overflow issue in batched inference for llama2
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args)
+        self.model = load_model(self.tokenizer, self.model_args, finetuning_args)
+        self.eval_template = get_eval_template(self.eval_args.lang)
+        self.choice_inputs = [self.tokenizer.encode(ch, add_special_tokens=False)[-1] for ch in CHOICES]
+
+    @torch.inference_mode()
+    def batch_inference(self, batch_input: dict[str, "torch.Tensor"]) -> list[str]:
+        logits = self.model(**batch_input).logits
+        lengths = torch.sum(batch_input["attention_mask"], dim=-1)
+        word_probs = torch.stack([logits[i, lengths[i] - 1] for i in range(len(lengths))], dim=0)
+        choice_probs = torch.nn.functional.softmax(word_probs[:, self.choice_inputs], dim=-1).detach()
+        return [chr(ord("A") + offset.item()) for offset in torch.argmax(choice_probs, dim=-1)]
+
+    def eval(self) -> None:
+        eval_task = self.eval_args.task.split("_")[0]
+        eval_split = self.eval_args.task.split("_")[1]
+
+        mapping = cached_file(
+            path_or_repo_id=os.path.join(self.eval_args.task_dir, eval_task),
+            filename="mapping.json",
+            cache_dir=self.model_args.cache_dir,
+            token=self.model_args.hf_hub_token,
+        )
+
+        with open(mapping, encoding="utf-8") as f:
+            categorys: dict[str, dict[str, str]] = json.load(f)
+
+        category_corrects = {subj: np.array([], dtype="bool") for subj in SUBJECTS}
+        pbar = tqdm(categorys.keys(), desc="Processing subjects", position=0)
+        results = {}
+        for subject in pbar:
+            dataset = load_dataset(
+                path=os.path.join(self.eval_args.task_dir, eval_task),
+                name=subject,
+                cache_dir=self.model_args.cache_dir,
+                download_mode=self.eval_args.download_mode,
+                token=self.model_args.hf_hub_token,
+                trust_remote_code=self.model_args.trust_remote_code,
+            )
+            pbar.set_postfix_str(categorys[subject]["name"])
+            inputs, outputs, labels = [], [], []
+            for i in trange(len(dataset[eval_split]), desc="Formatting batches", position=1, leave=False):
+                support_set = (
+                    dataset["train"].shuffle().select(range(min(self.eval_args.n_shot, len(dataset["train"]))))
+                )
+                messages = self.eval_template.format_example(
+                    target_data=dataset[eval_split][i],
+                    support_set=support_set,
+                    subject_name=categorys[subject]["name"],
+                )
+
+                input_ids, _ = self.template.encode_oneturn(tokenizer=self.tokenizer, messages=messages)
+                inputs.append({"input_ids": input_ids, "attention_mask": [1] * len(input_ids)})
+                labels.append(messages[-1]["content"])
+
+            for i in trange(
+                0, len(inputs), self.eval_args.batch_size, desc="Predicting batches", position=1, leave=False
+            ):
+                batch_input = self.tokenizer.pad(
+                    inputs[i : i + self.eval_args.batch_size], return_attention_mask=True, return_tensors="pt"
+                ).to(self.model.device)
+                preds = self.batch_inference(batch_input)
+                outputs += preds
+
+            corrects = np.array(outputs) == np.array(labels)
+            category_name = categorys[subject]["category"]
+            category_corrects[category_name] = np.concatenate([category_corrects[category_name], corrects], axis=0)
+            category_corrects["Average"] = np.concatenate([category_corrects["Average"], corrects], axis=0)
+            results[subject] = {str(i): outputs[i] for i in range(len(outputs))}
+
+        pbar.close()
+        self._save_results(category_corrects, results)
+
+    def _save_results(self, category_corrects: dict[str, "NDArray"], results: dict[str, dict[int, str]]) -> None:
+        score_info = "\n".join(
+            [
+                f"{category_name:>15}: {100 * np.mean(category_correct):.2f}"
+                for category_name, category_correct in category_corrects.items()
+                if len(category_correct)
+            ]
+        )
+        print(score_info)
+        if self.eval_args.save_dir is not None:
+            os.makedirs(self.eval_args.save_dir, exist_ok=False)
+            with open(os.path.join(self.eval_args.save_dir, "results.json"), "w", encoding="utf-8", newline="\n") as f:
+                json.dump(results, f, indent=2)
+
+            with open(os.path.join(self.eval_args.save_dir, "results.log"), "w", encoding="utf-8", newline="\n") as f:
+                f.write(score_info)
+
+
+def run_eval() -> None:
+    Evaluator().eval()

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/eval/template.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+from ..data import Role
+from ..extras.constants import CHOICES
+
+
+@dataclass
+class EvalTemplate:
+    system: str
+    choice: str
+    answer: str
+
+    def _parse_example(self, example: dict[str, str]) -> tuple[str, str]:
+        r"""Parse eval example.
+
+        input: a dict with keys {"question", "A", "B", "C", "D", "answer"}
+        output: a tuple of (prompt, response).
+        """
+        candidates = [self.choice.format(choice=ch, content=example[ch]) for ch in CHOICES if ch in example]
+        return "".join([example["question"]] + candidates + [self.answer]), example["answer"]
+
+    def format_example(
+        self, target_data: dict[str, str], support_set: list[dict[str, str]], subject_name: str
+    ) -> list[dict[str, str]]:
+        r"""Convert dataset examples to messages."""
+        messages = []
+        for k in range(len(support_set)):
+            prompt, response = self._parse_example(support_set[k])
+            messages.append({"role": Role.USER.value, "content": prompt})
+            messages.append({"role": Role.ASSISTANT.value, "content": response})
+
+        prompt, response = self._parse_example(target_data)
+        messages.append({"role": Role.USER.value, "content": prompt})
+        messages.append({"role": Role.ASSISTANT.value, "content": response})
+        messages[0]["content"] = self.system.format(subject=subject_name) + messages[0]["content"]
+        return messages
+
+
+eval_templates: dict[str, "EvalTemplate"] = {}
+
+
+def _register_eval_template(name: str, system: str, choice: str, answer: str) -> None:
+    eval_templates[name] = EvalTemplate(system=system, choice=choice, answer=answer)
+
+
+def get_eval_template(name: str) -> "EvalTemplate":
+    eval_template = eval_templates.get(name, None)
+    assert eval_template is not None, f"Template {name} does not exist."
+    return eval_template
+
+
+_register_eval_template(
+    name="en",
+    system="The following are multiple choice questions (with answers) about {subject}.\n\n",
+    choice="\n{choice}. {content}",
+    answer="\nAnswer:",
+)
+
+
+_register_eval_template(
+    name="zh",
+    system="以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n",
+    choice="\n{choice}. {content}",
+    answer="\n答案：",
+)

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/extras/constants.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from collections import OrderedDict, defaultdict
+from enum import Enum, unique
+from typing import Optional
+
+from peft.utils import SAFETENSORS_WEIGHTS_NAME as SAFE_ADAPTER_WEIGHTS_NAME
+from peft.utils import WEIGHTS_NAME as ADAPTER_WEIGHTS_NAME
+from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
+
+
+AUDIO_PLACEHOLDER = os.getenv("AUDIO_PLACEHOLDER", "<audio>")
+
+CHECKPOINT_NAMES = {
+    SAFE_ADAPTER_WEIGHTS_NAME,
+    ADAPTER_WEIGHTS_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
+}
+
+CHOICES = ["A", "B", "C", "D"]
+
+DATA_CONFIG = "dataset_info.json"
+
+DEFAULT_TEMPLATE = defaultdict(str)
+
+FILEEXT2TYPE = {
+    "arrow": "arrow",
+    "csv": "csv",
+    "json": "json",
+    "jsonl": "json",
+    "parquet": "parquet",
+    "txt": "text",
+}
+
+IGNORE_INDEX = -100
+
+IMAGE_PLACEHOLDER = os.getenv("IMAGE_PLACEHOLDER", "<image>")
+
+LAYERNORM_NAMES = {"norm", "ln"}
+
+LLAMABOARD_CONFIG = "llamaboard_config.yaml"
+
+METHODS = ["full", "freeze", "lora"]
+
+MOD_SUPPORTED_MODELS = {"bloom", "falcon", "gemma", "llama", "mistral", "mixtral", "phi", "starcoder2"}
+
+MULTIMODAL_SUPPORTED_MODELS = set()
+
+PEFT_METHODS = {"lora"}
+
+RUNNING_LOG = "running_log.txt"
+
+SUBJECTS = ["Average", "STEM", "Social Sciences", "Humanities", "Other"]
+
+SUPPORTED_MODELS = OrderedDict()
+
+TRAINER_LOG = "trainer_log.jsonl"
+
+TRAINING_ARGS = "training_args.yaml"
+
+TRAINING_STAGES = {
+    "Supervised Fine-Tuning": "sft",
+    "Reward Modeling": "rm",
+    "PPO": "ppo",
+    "DPO": "dpo",
+    "KTO": "kto",
+    "Pre-Training": "pt",
+}
+
+STAGES_USE_PAIR_DATA = {"rm", "dpo"}
+
+SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
+
+SWANLAB_CONFIG = "swanlab_public_config.json"
+
+VIDEO_PLACEHOLDER = os.getenv("VIDEO_PLACEHOLDER", "<video>")
+
+V_HEAD_WEIGHTS_NAME = "value_head.bin"
+
+V_HEAD_SAFE_WEIGHTS_NAME = "value_head.safetensors"
+
+
+class AttentionFunction(str, Enum):
+    AUTO = "auto"
+    DISABLED = "disabled"
+    SDPA = "sdpa"
+    FA2 = "fa2"
+
+
+class EngineName(str, Enum):
+    HF = "huggingface"
+    VLLM = "vllm"
+    SGLANG = "sglang"
+
+
+class DownloadSource(str, Enum):
+    DEFAULT = "hf"
+    MODELSCOPE = "ms"
+    OPENMIND = "om"
+
+
+@unique
+class QuantizationMethod(str, Enum):
+    r"""Borrowed from `transformers.utils.quantization_config.QuantizationMethod`."""
+
+    BNB = "bnb"
+    GPTQ = "gptq"
+    AWQ = "awq"
+    AQLM = "aqlm"
+    QUANTO = "quanto"
+    EETQ = "eetq"
+    HQQ = "hqq"
+
+
+class RopeScaling(str, Enum):
+    LINEAR = "linear"
+    DYNAMIC = "dynamic"
+    YARN = "yarn"
+    LLAMA3 = "llama3"
+
+
+def register_model_group(
+    models: dict[str, dict[DownloadSource, str]],
+    template: Optional[str] = None,
+    multimodal: bool = False,
+) -> None:
+    for name, path in models.items():
+        SUPPORTED_MODELS[name] = path
+        if template is not None and (
+            any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct")) or multimodal
+        ):
+            DEFAULT_TEMPLATE[name] = template
+
+        if multimodal:
+            MULTIMODAL_SUPPORTED_MODELS.add(name)
+
+
+register_model_group(
+    models={
+        "Aya-23-8B-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/aya-23-8B",
+        },
+        "Aya-23-35B-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/aya-23-35B",
+        },
+    },
+    template="cohere",
+)
+
+
+register_model_group(
+    models={
+        "Baichuan-7B-Base": {
+            DownloadSource.DEFAULT: "baichuan-inc/Baichuan-7B",
+            DownloadSource.MODELSCOPE: "baichuan-inc/baichuan-7B",
+        },
+        "Baichuan-13B-Base": {
+            DownloadSource.DEFAULT: "baichuan-inc/Baichuan-13B-Base",
+            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan-13B-Base",
+        },
+        "Baichuan-13B-Chat": {
+            DownloadSource.DEFAULT: "baichuan-inc/Baichuan-13B-Chat",
+            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan-13B-Chat",
+        },
+    },
+    template="baichuan",
+)
+
+
+register_model_group(
+    models={
+        "Baichuan2-7B-Base": {
+            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-7B-Base",
+            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-7B-Base",
+        },
+        "Baichuan2-13B-Base": {
+            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Base",
+            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Base",
+            DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_base_pt",
+        },
+        "Baichuan2-7B-Chat": {
+            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-7B-Chat",
+            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-7B-Chat",
+            DownloadSource.OPENMIND: "Baichuan/Baichuan2_7b_chat_pt",
+        },
+        "Baichuan2-13B-Chat": {
+            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Chat",
+            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Chat",
+            DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_chat_pt",
+        },
+    },
+    template="baichuan2",
+)
+
+
+register_model_group(
+    models={
+        "BLOOM-560M": {
+            DownloadSource.DEFAULT: "bigscience/bloom-560m",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/bloom-560m",
+        },
+        "BLOOM-3B": {
+            DownloadSource.DEFAULT: "bigscience/bloom-3b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/bloom-3b",
+        },
+        "BLOOM-7B1": {
+            DownloadSource.DEFAULT: "bigscience/bloom-7b1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/bloom-7b1",
+        },
+    },
+)
+
+
+register_model_group(
+    models={
+        "BLOOMZ-560M": {
+            DownloadSource.DEFAULT: "bigscience/bloomz-560m",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/bloomz-560m",
+        },
+        "BLOOMZ-3B": {
+            DownloadSource.DEFAULT: "bigscience/bloomz-3b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/bloomz-3b",
+        },
+        "BLOOMZ-7B1-mt": {
+            DownloadSource.DEFAULT: "bigscience/bloomz-7b1-mt",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/bloomz-7b1-mt",
+        },
+    },
+)
+
+
+register_model_group(
+    models={
+        "BlueLM-7B-Base": {
+            DownloadSource.DEFAULT: "vivo-ai/BlueLM-7B-Base",
+            DownloadSource.MODELSCOPE: "vivo-ai/BlueLM-7B-Base",
+        },
+        "BlueLM-7B-Chat": {
+            DownloadSource.DEFAULT: "vivo-ai/BlueLM-7B-Chat",
+            DownloadSource.MODELSCOPE: "vivo-ai/BlueLM-7B-Chat",
+        },
+    },
+    template="bluelm",
+)
+
+
+register_model_group(
+    models={
+        "Breeze-7B": {
+            DownloadSource.DEFAULT: "MediaTek-Research/Breeze-7B-Base-v1_0",
+        },
+        "Breeze-7B-Instruct": {
+            DownloadSource.DEFAULT: "MediaTek-Research/Breeze-7B-Instruct-v1_0",
+        },
+    },
+    template="breeze",
+)
+
+
+register_model_group(
+    models={
+        "ChatGLM2-6B-Chat": {
+            DownloadSource.DEFAULT: "THUDM/chatglm2-6b",
+            DownloadSource.MODELSCOPE: "ZhipuAI/chatglm2-6b",
+        }
+    },
+    template="chatglm2",
+)
+
+
+register_model_group(
+    models={
+        "ChatGLM3-6B-Base": {
+            DownloadSource.DEFAULT: "THUDM/chatglm3-6b-base",
+            DownloadSource.MODELSCOPE: "ZhipuAI/chatglm3-6b-base",
+        },
+        "ChatGLM3-6B-Chat": {
+            DownloadSource.DEFAULT: "THUDM/chatglm3-6b",
+            DownloadSource.MODELSCOPE: "ZhipuAI/chatglm3-6b",
+        },
+    },
+    template="chatglm3",
+)
+
+
+register_model_group(
+    models={
+        "Chinese-Llama-2-1.3B": {
+            DownloadSource.DEFAULT: "hfl/chinese-llama-2-1.3b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-1.3b",
+        },
+        "Chinese-Llama-2-7B": {
+            DownloadSource.DEFAULT: "hfl/chinese-llama-2-7b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-7b",
+        },
+        "Chinese-Llama-2-13B": {
+            DownloadSource.DEFAULT: "hfl/chinese-llama-2-13b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-13b",
+        },
+        "Chinese-Alpaca-2-1.3B-Chat": {
+            DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-1.3b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-1.3b",
+        },
+        "Chinese-Alpaca-2-7B-Chat": {
+            DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-7b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-7b",
+        },
+        "Chinese-Alpaca-2-13B-Chat": {
+            DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-13b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-13b",
+        },
+    },
+    template="llama2_zh",
+)
+
+
+register_model_group(
+    models={
+        "CodeGeeX4-9B-Chat": {
+            DownloadSource.DEFAULT: "THUDM/codegeex4-all-9b",
+            DownloadSource.MODELSCOPE: "ZhipuAI/codegeex4-all-9b",
+        },
+    },
+    template="codegeex4",
+)
+
+
+register_model_group(
+    models={
+        "CodeGemma-7B": {
+            DownloadSource.DEFAULT: "google/codegemma-7b",
+        },
+        "CodeGemma-7B-Instruct": {
+            DownloadSource.DEFAULT: "google/codegemma-7b-it",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/codegemma-7b-it",
+        },
+        "CodeGemma-1.1-2B": {
+            DownloadSource.DEFAULT: "google/codegemma-1.1-2b",
+        },
+        "CodeGemma-1.1-7B-Instruct": {
+            DownloadSource.DEFAULT: "google/codegemma-1.1-7b-it",
+        },
+    },
+    template="gemma",
+)
+
+
+register_model_group(
+    models={
+        "Codestral-22B-v0.1-Chat": {
+            DownloadSource.DEFAULT: "mistralai/Codestral-22B-v0.1",
+            DownloadSource.MODELSCOPE: "swift/Codestral-22B-v0.1",
+        },
+    },
+    template="mistral",
+)
+
+
+register_model_group(
+    models={
+        "CommandR-35B-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-v01",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/c4ai-command-r-v01",
+        },
+        "CommandR-Plus-104B-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-plus",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/c4ai-command-r-plus",
+        },
+        "CommandR-35B-4bit-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-v01-4bit",
+            DownloadSource.MODELSCOPE: "mirror013/c4ai-command-r-v01-4bit",
+        },
+        "CommandR-Plus-104B-4bit-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-plus-4bit",
+        },
+    },
+    template="cohere",
+)
+
+
+register_model_group(
+    models={
+        "DBRX-132B-Base": {
+            DownloadSource.DEFAULT: "databricks/dbrx-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/dbrx-base",
+        },
+        "DBRX-132B-Instruct": {
+            DownloadSource.DEFAULT: "databricks/dbrx-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/dbrx-instruct",
+        },
+    },
+    template="dbrx",
+)
+
+
+register_model_group(
+    models={
+        "DeepSeek-LLM-7B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-llm-7b-base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-llm-7b-base",
+        },
+        "DeepSeek-LLM-67B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-llm-67b-base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-llm-67b-base",
+        },
+        "DeepSeek-LLM-7B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-llm-7b-chat",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-llm-7b-chat",
+        },
+        "DeepSeek-LLM-67B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-llm-67b-chat",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-llm-67b-chat",
+        },
+        "DeepSeek-Math-7B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-math-7b-base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-math-7b-base",
+        },
+        "DeepSeek-Math-7B-Instruct": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-math-7b-instruct",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-math-7b-instruct",
+        },
+        "DeepSeek-MoE-16B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-moe-16b-base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-moe-16b-base",
+        },
+        "DeepSeek-MoE-16B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-moe-16b-chat",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-moe-16b-chat",
+        },
+        "DeepSeek-V2-16B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Lite",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Lite",
+        },
+        "DeepSeek-V2-236B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2",
+        },
+        "DeepSeek-V2-16B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Lite-Chat",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Lite-Chat",
+        },
+        "DeepSeek-V2-236B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Chat",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Chat",
+        },
+        "DeepSeek-Coder-V2-16B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Lite-Base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-Coder-V2-Lite-Base",
+        },
+        "DeepSeek-Coder-V2-236B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-Coder-V2-Base",
+        },
+        "DeepSeek-Coder-V2-16B-Instruct": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
+        },
+        "DeepSeek-Coder-V2-236B-Instruct": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Instruct",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-Coder-V2-Instruct",
+        },
+    },
+    template="deepseek",
+)
+
+
+register_model_group(
+    models={
+        "DeepSeek-Coder-6.7B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-base",
+        },
+        "DeepSeek-Coder-7B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-base-v1.5",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-7b-base-v1.5",
+        },
+        "DeepSeek-Coder-33B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-base",
+        },
+        "DeepSeek-Coder-6.7B-Instruct": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-instruct",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-instruct",
+        },
+        "DeepSeek-Coder-7B-Instruct": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
+        },
+        "DeepSeek-Coder-33B-Instruct": {
+            DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-instruct",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-instruct",
+        },
+    },
+    template="deepseekcoder",
+)
+
+
+register_model_group(
+    models={
+        "DeepSeek-V2-0628-236B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Chat-0628",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Chat-0628",
+        },
+        "DeepSeek-V2.5-236B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2.5",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2.5",
+        },
+        "DeepSeek-V2.5-1210-236B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2.5-1210",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2.5-1210",
+        },
+        "DeepSeek-V3-671B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V3-Base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V3-Base",
+        },
+        "DeepSeek-V3-671B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V3",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V3",
+        },
+        "DeepSeek-V3-0324-671B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V3-0324",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V3-0324",
+        },
+    },
+    template="deepseek3",
+)
+
+
+register_model_group(
+    models={
+        "DeepSeek-R1-1.5B-Distill": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+        },
+        "DeepSeek-R1-7B-Distill": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
+        },
+        "DeepSeek-R1-8B-Distill": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
+        },
+        "DeepSeek-R1-14B-Distill": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
+        },
+        "DeepSeek-R1-32B-Distill": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+        },
+        "DeepSeek-R1-70B-Distill": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+        },
+        "DeepSeek-R1-671B-Chat-Zero": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Zero",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Zero",
+        },
+        "DeepSeek-R1-671B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1",
+        },
+        "DeepSeek-R1-0528-8B-Distill": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
+        },
+        "DeepSeek-R1-0528-671B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-0528",
+            DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-0528",
+        },
+    },
+    template="deepseekr1",
+)
+
+
+register_model_group(
+    models={
+        "Devstral-Small-2507-Instruct": {
+            DownloadSource.DEFAULT: "mistralai/Devstral-Small-2507",
+            DownloadSource.MODELSCOPE: "mistralai/Devstral-Small-2507",
+        },
+    },
+    template="mistral_small",
+)
+
+
+register_model_group(
+    models={
+        "EXAONE-3.0-7.8B-Instruct": {
+            DownloadSource.DEFAULT: "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
+        },
+    },
+    template="exaone",
+)
+
+
+register_model_group(
+    models={
+        "Falcon-7B": {
+            DownloadSource.DEFAULT: "tiiuae/falcon-7b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/falcon-7b",
+        },
+        "Falcon-11B": {
+            DownloadSource.DEFAULT: "tiiuae/falcon-11B",
+            DownloadSource.MODELSCOPE: "tiiuae/falcon-11B",
+        },
+        "Falcon-40B": {
+            DownloadSource.DEFAULT: "tiiuae/falcon-40b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/falcon-40b",
+        },
+        "Falcon-180B": {
+            DownloadSource.DEFAULT: "tiiuae/falcon-180b",
+            DownloadSource.MODELSCOPE: "modelscope/falcon-180B",
+        },
+        "Falcon-7B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/falcon-7b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/falcon-7b-instruct",
+        },
+        "Falcon-40B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/falcon-40b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/falcon-40b-instruct",
+        },
+        "Falcon-180B-Chat": {
+            DownloadSource.DEFAULT: "tiiuae/falcon-180b-chat",
+            DownloadSource.MODELSCOPE: "modelscope/falcon-180B-chat",
+        },
+    },
+    template="falcon",
+)
+
+register_model_group(
+    models={
+        "Falcon-H1-0.5B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-0.5B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-0.5B-Base",
+        },
+        "Falcon-H1-1.5B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Base",
+        },
+        "Falcon-H1-1.5B-Deep-Base": {
+            DownloadSource.DEFAULT: "tiuae/Falcon-H1-1.5B-Deep-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Deep-Base",
+        },
+        "Falcon-H1-3B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-3B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-3B-Base",
+        },
+        "Falcon-H1-7B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-7B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-7B-Base",
+        },
+        "Falcon-H1-34B-Base": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-34B-Base",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-34B-Base",
+        },
+        "Falcon-H1-0.5B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-0.5B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-0.5B-Instruct",
+        },
+        "Falcon-H1-1.5B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Instruct",
+        },
+        "Falcon-H1-1.5B-Deep-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
+        },
+        "Falcon-H1-3B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-3B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-3B-Instruct",
+        },
+        "Falcon-H1-7B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-7B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-7B-Instruct",
+        },
+        "Falcon-H1-34B-Instruct": {
+            DownloadSource.DEFAULT: "tiiuae/Falcon-H1-34B-Instruct",
+            DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-34B-Instruct",
+        },
+    },
+    template="falcon_h1",
+)
+
+
+register_model_group(
+    models={
+        "Gemma-2B": {
+            DownloadSource.DEFAULT: "google/gemma-2b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/gemma-2b",
+        },
+        "Gemma-7B": {
+            DownloadSource.DEFAULT: "google/gemma-7b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/gemma-2b-it",
+        },
+        "Gemma-2B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-2b-it",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/gemma-7b",
+        },
+        "Gemma-7B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-7b-it",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/gemma-7b-it",
+        },
+        "Gemma-1.1-2B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-1.1-2b-it",
+        },
+        "Gemma-1.1-7B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-1.1-7b-it",
+        },
+    },
+    template="gemma",
+)
+
+
+register_model_group(
+    models={
+        "Gemma-2-2B": {
+            DownloadSource.DEFAULT: "google/gemma-2-2b",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b",
+        },
+        "Gemma-2-9B": {
+            DownloadSource.DEFAULT: "google/gemma-2-9b",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-9b",
+        },
+        "Gemma-2-27B": {
+            DownloadSource.DEFAULT: "google/gemma-2-27b",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-27b",
+        },
+        "Gemma-2-2B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-2-2b-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b-it",
+            DownloadSource.OPENMIND: "LlamaFactory/gemma-2-2b-it",
+        },
+        "Gemma-2-9B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-2-9b-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-9b-it",
+            DownloadSource.OPENMIND: "LlamaFactory/gemma-2-9b-it",
+        },
+        "Gemma-2-27B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-2-27b-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-27b-it",
+        },
+        "Gemma-3-1B": {
+            DownloadSource.DEFAULT: "google/gemma-3-1b-pt",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-1b-pt",
+        },
+        "Gemma-3-1B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3-1b-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-1b-it",
+        },
+        "MedGemma-27B-Instruct": {
+            DownloadSource.DEFAULT: "google/medgemma-27b-text-it",
+            DownloadSource.MODELSCOPE: "google/medgemma-27b-text-it",
+        },
+    },
+    template="gemma2",
+)
+
+
+register_model_group(
+    models={
+        "Gemma-3-4B": {
+            DownloadSource.DEFAULT: "google/gemma-3-4b-pt",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-4b-pt",
+        },
+        "Gemma-3-12B": {
+            DownloadSource.DEFAULT: "google/gemma-3-12b-pt",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-12b-pt",
+        },
+        "Gemma-3-27B": {
+            DownloadSource.DEFAULT: "google/gemma-3-27b-pt",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-27b-pt",
+        },
+        "Gemma-3-4B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3-4b-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-4b-it",
+        },
+        "Gemma-3-12B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3-12b-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-12b-it",
+        },
+        "Gemma-3-27B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3-27b-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-27b-it",
+        },
+        "MedGemma-4B": {
+            DownloadSource.DEFAULT: "google/medgemma-4b-pt",
+            DownloadSource.MODELSCOPE: "google/medgemma-4b-pt",
+        },
+        "MedGemma-4B-Instruct": {
+            DownloadSource.DEFAULT: "google/medgemma-4b-it",
+            DownloadSource.MODELSCOPE: "google/medgemma-4b-it",
+        },
+    },
+    template="gemma3",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Gemma-3n-E2B": {
+            DownloadSource.DEFAULT: "google/gemma-3n-E2B",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E2B",
+        },
+        "Gemma-3n-E4B": {
+            DownloadSource.DEFAULT: "google/gemma-3n-E4B",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E4B",
+        },
+        "Gemma-3n-E2B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3n-E2B-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E2B-it",
+        },
+        "Gemma-3n-E4B-Instruct": {
+            DownloadSource.DEFAULT: "google/gemma-3n-E4B-it",
+            DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E4B-it",
+        },
+    },
+    template="gemma3n",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "GLM-4-9B": {
+            DownloadSource.DEFAULT: "THUDM/glm-4-9b",
+            DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b",
+        },
+        "GLM-4-9B-Chat": {
+            DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat",
+            DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat",
+            DownloadSource.OPENMIND: "LlamaFactory/glm-4-9b-chat",
+        },
+        "GLM-4-9B-1M-Chat": {
+            DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat-1m",
+            DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat-1m",
+        },
+        "GLM-4-0414-9B-Chat": {
+            DownloadSource.DEFAULT: "THUDM/GLM-4-9B-0414",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-9B-0414",
+        },
+        "GLM-4-0414-32B-Base": {
+            DownloadSource.DEFAULT: "THUDM/GLM-4-32B-Base-0414",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-32B-Base-0414",
+        },
+        "GLM-4-0414-32B-Chat": {
+            DownloadSource.DEFAULT: "THUDM/GLM-4-32B-0414",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-32B-0414",
+        },
+    },
+    template="glm4",
+)
+
+
+register_model_group(
+    models={
+        "GLM-4.1V-9B-Base": {
+            DownloadSource.DEFAULT: "THUDM/GLM-4.1V-9B-Base",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.1V-9B-Base",
+        },
+        "GLM-4.1V-9B-Thinking": {
+            DownloadSource.DEFAULT: "THUDM/GLM-4.1V-9B-Thinking",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.1V-9B-Thinking",
+        },
+    },
+    template="glm4v",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "GLM-Z1-0414-9B-Chat": {
+            DownloadSource.DEFAULT: "THUDM/GLM-Z1-9B-0414",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-Z1-9B-0414",
+        },
+        "GLM-Z1-0414-32B-Chat": {
+            DownloadSource.DEFAULT: "THUDM/GLM-Z1-32B-0414",
+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-Z1-32B-0414",
+        },
+    },
+    template="glmz1",
+)
+
+
+register_model_group(
+    models={
+        "GPT-2-Small": {
+            DownloadSource.DEFAULT: "openai-community/gpt2",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/gpt2",
+        },
+        "GPT-2-Medium": {
+            DownloadSource.DEFAULT: "openai-community/gpt2-medium",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/gpt2-medium",
+        },
+        "GPT-2-Large": {
+            DownloadSource.DEFAULT: "openai-community/gpt2-large",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/gpt2-large",
+        },
+        "GPT-2-XL": {
+            DownloadSource.DEFAULT: "openai-community/gpt2-xl",
+            DownloadSource.MODELSCOPE: "goodbai95/GPT2-xl",
+        },
+    },
+)
+
+
+register_model_group(
+    models={
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+            DownloadSource.DEFAULT: "ibm-granite/granite-3.0-1b-a400m-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-1b-a400m-base",
+        },
+        "Granite-3.0-3B-A800M-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.0-3b-a800m-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-3b-a800m-base",
+        },
+        "Granite-3.0-2B-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.0-2b-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-2b-base",
+        },
+        "Granite-3.0-8B-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.0-8b-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-8b-base",
+        },
+        "Granite-3.0-1B-A400M-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.0-1b-a400m-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-1b-a400m-instruct",
+        },
+        "Granite-3.0-3B-A800M-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.0-3b-a800m-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-3b-a800m-instruct",
+        },
+        "Granite-3.0-2B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.0-2b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-2b-instruct",
+        },
+        "Granite-3.0-8B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.0-8b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-8b-instruct",
+        },
+        "Granite-3.1-1B-A400M-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.1-1b-a400m-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-1b-a400m-base",
+        },
+        "Granite-3.1-3B-A800M-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.1-3b-a800m-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-3b-a800m-base",
+        },
+        "Granite-3.1-2B-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.1-2b-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-2b-base",
+        },
+        "Granite-3.1-8B-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.1-8b-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-8b-base",
+        },
+        "Granite-3.1-1B-A400M-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.1-1b-a400m-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-1b-a400m-instruct",
+        },
+        "Granite-3.1-3B-A800M-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.1-3b-a800m-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-3b-a800m-instruct",
+        },
+        "Granite-3.1-2B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.1-2b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-2b-instruct",
+        },
+        "Granite-3.1-8B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.1-8b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-8b-instruct",
+        },
+        "Granite-3.2-2B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.2-2b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.2-2b-instruct",
+        },
+        "Granite-3.2-8B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.2-8b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.2-8b-instruct",
+        },
+        "Granite-3.3-2B-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.3-2b-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-2b-base",
+        },
+        "Granite-3.3-8B-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.3-8b-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-8b-base",
+        },
+        "Granite-3.3-2B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.3-2b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-2b-instruct",
+        },
+        "Granite-3.3-8B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.3-8b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-8b-instruct",
+        },
+    },
+    template="granite3",
+)
+
+
+register_model_group(
+    models={
+        "Granite-Vision-3.2-2B": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-vision-3.2-2b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-vision-3.2-2b",
+        },
+    },
+    template="granite3_vision",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Hunyuan-7B-Instruct": {
+            DownloadSource.DEFAULT: "tencent/Hunyuan-7B-Instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Hunyuan-7B-Instruct",
+        },
+    },
+    template="hunyuan",
+)
+
+
+register_model_group(
+    models={
+        "Index-1.9B-Base": {
+            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B",
+            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B",
+        },
+        "Index-1.9B-Base-Pure": {
+            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Pure",
+            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Pure",
+        },
+        "Index-1.9B-Chat": {
+            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Chat",
+            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Chat",
+        },
+        "Index-1.9B-Character-Chat": {
+            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Character",
+            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Character",
+        },
+        "Index-1.9B-Chat-32K": {
+            DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-32K",
+            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-32K",
+        },
+    },
+    template="index",
+)
+
+
+register_model_group(
+    models={
+        "InternLM-7B": {
+            DownloadSource.DEFAULT: "internlm/internlm-7b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm-7b",
+        },
+        "InternLM-20B": {
+            DownloadSource.DEFAULT: "internlm/internlm-20b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm-20b",
+        },
+        "InternLM-7B-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm-chat-7b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm-chat-7b",
+        },
+        "InternLM-20B-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm-chat-20b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm-chat-20b",
+        },
+    },
+    template="intern",
+)
+
+
+register_model_group(
+    models={
+        "InternLM2-7B": {
+            DownloadSource.DEFAULT: "internlm/internlm2-7b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-7b",
+        },
+        "InternLM2-20B": {
+            DownloadSource.DEFAULT: "internlm/internlm2-20b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-20b",
+        },
+        "InternLM2-7B-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm2-chat-7b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-chat-7b",
+        },
+        "InternLM2-20B-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm2-chat-20b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-chat-20b",
+        },
+        "InternLM2.5-1.8B": {
+            DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b",
+        },
+        "InternLM2.5-7B": {
+            DownloadSource.DEFAULT: "internlm/internlm2_5-7b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b",
+        },
+        "InternLM2.5-20B": {
+            DownloadSource.DEFAULT: "internlm/internlm2_5-20b",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-20b",
+        },
+        "InternLM2.5-1.8B-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b-chat",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b-chat",
+        },
+        "InternLM2.5-7B-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat",
+        },
+        "InternLM2.5-7B-1M-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat-1m",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat-1m",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat-1m",
+        },
+        "InternLM2.5-20B-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm2_5-20b-chat",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-20b-chat",
+        },
+        "InternLM3-8B-Chat": {
+            DownloadSource.DEFAULT: "internlm/internlm3-8b-instruct",
+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm3-8b-instruct",
+        },
+    },
+    template="intern2",
+)
+
+
+register_model_group(
+    models={
+        "InternVL2.5-2B-MPO": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL2_5-2B-MPO-hf",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL2_5-2B-MPO-hf",
+        },
+        "InternVL2.5-8B-MPO": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL2_5-8B-MPO-hf",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL2_5-8B-MPO-hf",
+        },
+        "InternVL3-1B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3-1B-hf",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-1B-hf",
+        },
+        "InternVL3-2B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3-2B-hf",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-2B-hf",
+        },
+        "InternVL3-8B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3-8B-hf",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-8B-hf",
+        },
+        "InternVL3-14B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3-14B-hf",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-14B-hf",
+        },
+        "InternVL3-38B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3-38B-hf",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-38B-hf",
+        },
+        "InternVL3-78B-hf": {
+            DownloadSource.DEFAULT: "OpenGVLab/InternVL3-78B-hf",
+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-78B-hf",
+        },
+    },
+    template="intern_vl",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Jamba-v0.1": {
+            DownloadSource.DEFAULT: "ai21labs/Jamba-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Jamba-v0.1",
+        }
+    },
+)
+
+
+register_model_group(
+    models={
+        "Kimi-Dev-72B-Instruct": {
+            DownloadSource.DEFAULT: "moonshotai/Kimi-Dev-72B",
+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-Dev-72B",
+        },
+    },
+    template="qwen",
+)
+
+
+register_model_group(
+    models={
+        "Kimi-VL-A3B-Instruct": {
+            DownloadSource.DEFAULT: "moonshotai/Kimi-VL-A3B-Instruct",
+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Instruct",
+        },
+        "Kimi-VL-A3B-Thinking": {
+            DownloadSource.DEFAULT: "moonshotai/Kimi-VL-A3B-Thinking",
+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Thinking",
+        },
+        "Kimi-VL-A3B-Thinking-2506": {
+            DownloadSource.DEFAULT: "moonshotai/Kimi-VL-A3B-Thinking-2506",
+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Thinking-2506",
+        },
+    },
+    template="kimi_vl",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "LingoWhale-8B": {
+            DownloadSource.DEFAULT: "deeplang-ai/LingoWhale-8B",
+            DownloadSource.MODELSCOPE: "DeepLang/LingoWhale-8B",
+        }
+    },
+)
+
+
+register_model_group(
+    models={
+        "Llama-7B": {
+            DownloadSource.DEFAULT: "huggyllama/llama-7b",
+            DownloadSource.MODELSCOPE: "skyline2006/llama-7b",
+        },
+        "Llama-13B": {
+            DownloadSource.DEFAULT: "huggyllama/llama-13b",
+            DownloadSource.MODELSCOPE: "skyline2006/llama-13b",
+        },
+        "Llama-30B": {
+            DownloadSource.DEFAULT: "huggyllama/llama-30b",
+            DownloadSource.MODELSCOPE: "skyline2006/llama-30b",
+        },
+        "Llama-65B": {
+            DownloadSource.DEFAULT: "huggyllama/llama-65b",
+            DownloadSource.MODELSCOPE: "skyline2006/llama-65b",
+        },
+    }
+)
+
+
+register_model_group(
+    models={
+        "Llama-2-7B": {
+            DownloadSource.DEFAULT: "meta-llama/Llama-2-7b-hf",
+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-ms",
+        },
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+        },
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-ms",
+        },
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-chat-ms",
+        },
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-chat-ms",
+        },
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-chat-ms",
+        },
+    },
+    template="llama2",
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B-Instruct",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B-Instruct",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama3-8B-Chinese-Chat",
+            DownloadSource.OPENMIND: "LlamaFactory/Llama3-Chinese-8B-Instruct",
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+        },
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-8B",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-70B",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-405B",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-8B-Instruct",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-70B-Instruct",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-405B-Instruct",
+        },
+        "Llama-3.1-8B-Chinese-Chat": {
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+            DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-8B-Chinese-Chat",
+        },
+        "Llama-3.1-70B-Chinese-Chat": {
+            DownloadSource.DEFAULT: "shenzhi-wang/Llama3.1-70B-Chinese-Chat",
+            DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-70B-Chinese-Chat",
+        },
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-1B",
+        },
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-3B",
+        },
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-1B-Instruct",
+        },
+        "Llama-3.2-3B-Instruct": {
+            DownloadSource.DEFAULT: "meta-llama/Llama-3.2-3B-Instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-3B-Instruct",
+        },
+        "Llama-3.3-70B-Instruct": {
+            DownloadSource.DEFAULT: "meta-llama/Llama-3.3-70B-Instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.3-70B-Instruct",
+        },
+    },
+    template="llama3",
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-11B-Vision",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-11B-Vision-Instruct",
+        },
+        "Llama-3.2-90B-Vision": {
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-90B-Vision",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-90B-Vision-Instruct",
+        },
+    },
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+    multimodal=True,
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-4-Scout-17B-16E",
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-4-Scout-17B-16E-Instruct",
+        },
+        "Llama-4-Maverick-17B-128E": {
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-4-Maverick-17B-128E",
+        },
+        "Llama-4-Maverick-17B-128E-Instruct": {
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+            DownloadSource.MODELSCOPE: "LLM-Research/Llama-4-Maverick-17B-128E-Instruct",
+        },
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+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "swift/llava-1.5-7b-hf",
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+            DownloadSource.MODELSCOPE: "swift/llava-1.5-13b-hf",
+        },
+    },
+    template="llava",
+    multimodal=True,
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "swift/llava-v1.6-vicuna-7b-hf",
+        },
+        "LLaVA-NeXT-13B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/llava-v1.6-vicuna-13b-hf",
+            DownloadSource.MODELSCOPE: "swift/llava-v1.6-vicuna-13b-hf",
+        },
+    },
+    template="llava_next",
+    multimodal=True,
+)
+
+
+register_model_group(
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+        "LLaVA-NeXT-Mistral-7B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/llava-v1.6-mistral-7b-hf",
+            DownloadSource.MODELSCOPE: "swift/llava-v1.6-mistral-7b-hf",
+        },
+    },
+    template="llava_next_mistral",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "LLaVA-NeXT-Llama3-8B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/llama3-llava-next-8b-hf",
+            DownloadSource.MODELSCOPE: "swift/llama3-llava-next-8b-hf",
+        },
+    },
+    template="llava_next_llama3",
+    multimodal=True,
+)
+
+
+register_model_group(
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+        "LLaVA-NeXT-34B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/llava-v1.6-34b-hf",
+            DownloadSource.MODELSCOPE: "LLM-Research/llava-v1.6-34b-hf",
+        },
+    },
+    template="llava_next_yi",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
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+            DownloadSource.DEFAULT: "llava-hf/llava-next-72b-hf",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/llava-next-72b-hf",
+        },
+        "LLaVA-NeXT-110B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/llava-next-110b-hf",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/llava-next-110b-hf",
+        },
+    },
+    template="llava_next_qwen",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "LLaVA-NeXT-Video-7B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-hf",
+            DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-hf",
+        },
+        "LLaVA-NeXT-Video-7B-DPO-Chat": {
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+            DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-DPO-hf",
+        },
+    },
+    template="llava_next_video",
+    multimodal=True,
+)
+
+
+register_model_group(
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+        "LLaVA-NeXT-Video-7B-32k-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-32K-hf",
+            DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-32K-hf",
+        },
+    },
+    template="llava_next_video_mistral",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "LLaVA-NeXT-Video-34B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-34B-hf",
+            DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-34B-hf",
+        },
+        "LLaVA-NeXT-Video-34B-DPO-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-34B-DPO-hf",
+        },
+    },
+    template="llava_next_video_yi",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Marco-o1-Chat": {
+            DownloadSource.DEFAULT: "AIDC-AI/Marco-o1",
+            DownloadSource.MODELSCOPE: "AIDC-AI/Marco-o1",
+        },
+    },
+    template="marco",
+)
+
+
+register_model_group(
+    models={
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+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-7B-Base",
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-7B-Base",
+        },
+        "MiMo-7B-Instruct": {
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-7B-SFT",
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-7B-SFT",
+        },
+        "MiMo-7B-Instruct-RL": {
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-7B-RL",
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-7B-RL",
+        },
+        "MiMo-7B-RL-ZERO": {
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-7B-RL-ZERO",
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-7B-RL-ZERO",
+        },
+    },
+    template="mimo",
+)
+
+
+register_model_group(
+    models={
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+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-SFT",
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-VL-7B-SFT",
+        },
+        "MiMo-7B-VL-RL": {
+            DownloadSource.DEFAULT: "XiaomiMiMo/MiMo-VL-7B-RL",
+            DownloadSource.MODELSCOPE: "XiaomiMiMo/MiMo-VL-7B-RL",
+        },
+    },
+    template="mimo_vl",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "MiniCPM-2B-SFT-Chat": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM-2B-sft-bf16",
+            DownloadSource.MODELSCOPE: "OpenBMB/miniCPM-bf16",
+        },
+        "MiniCPM-2B-DPO-Chat": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM-2B-dpo-bf16",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-2B-dpo-bf16",
+        },
+    },
+    template="cpm",
+)
+
+
+register_model_group(
+    models={
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+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM3-4B",
+            DownloadSource.OPENMIND: "LlamaFactory/MiniCPM3-4B",
+        },
+    },
+    template="cpm3",
+)
+
+
+register_model_group(
+    models={
+        "MiniCPM4-0.5B-Chat": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM4-0.5B",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM4-0.5B",
+        },
+        "MiniCPM4-8B-Chat": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM4-8B",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM4-8B",
+        },
+    },
+    template="cpm4",
+)
+
+
+register_model_group(
+    models={
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+            DownloadSource.DEFAULT: "openbmb/MiniCPM-o-2_6",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-o-2_6",
+        },
+    },
+    template="minicpm_o",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "MiniCPM-V-2_6": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM-V-2_6",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-V-2_6",
+        },
+    },
+    template="minicpm_v",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
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+            DownloadSource.DEFAULT: "mistralai/Ministral-8B-Instruct-2410",
+            DownloadSource.MODELSCOPE: "mistralai/Ministral-8B-Instruct-2410",
+        },
+        "Mistral-Nemo-Base-2407": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-Nemo-Base-2407",
+            DownloadSource.MODELSCOPE: "LLM-Research/Mistral-Nemo-Base-2407",
+        },
+        "Mistral-Nemo-Instruct-2407": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-Nemo-Instruct-2407",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mistral-Nemo-Instruct-2407",
+        },
+    },
+    template="ministral",
+)
+
+
+register_model_group(
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+            DownloadSource.DEFAULT: "mistralai/Mistral-7B-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mistral-7B-v0.1",
+        },
+        "Mistral-7B-v0.2": {
+            DownloadSource.DEFAULT: "alpindale/Mistral-7B-v0.2-hf",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mistral-7B-v0.2-hf",
+        },
+        "Mistral-7B-v0.3": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-7B-v0.3",
+            DownloadSource.MODELSCOPE: "LLM-Research/mistral-7b-v0.3",
+        },
+        "Mistral-7B-Instruct-v0.1": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-7B-Instruct-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mistral-7B-Instruct-v0.1",
+        },
+        "Mistral-7B-Instruct-v0.2": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-7B-Instruct-v0.2",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mistral-7B-Instruct-v0.2",
+        },
+        "Mistral-7B-Instruct-v0.3": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-7B-Instruct-v0.3",
+            DownloadSource.MODELSCOPE: "LLM-Research/Mistral-7B-Instruct-v0.3",
+        },
+    },
+    template="mistral",
+)
+
+
+register_model_group(
+    models={
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+            DownloadSource.DEFAULT: "mistralai/Mistral-Small-24B-Base-2501",
+            DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-24B-Base-2501",
+        },
+        "Mistral-Small-24B-Instruct-2501": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-Small-24B-Instruct-2501",
+            DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-24B-Instruct-2501",
+        },
+    },
+    template="mistral_small",
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-3.1-24B-Base-2503",
+        },
+        "Mistral-Small-3.1-24B-Instruct": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
+            DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
+        },
+        "Mistral-Small-3.2-24B-Instruct": {
+            DownloadSource.DEFAULT: "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
+            DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
+        },
+    },
+    template="mistral_small",
+    multimodal=True,
+)
+
+
+register_model_group(
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+        "Mixtral-8x7B-v0.1": {
+            DownloadSource.DEFAULT: "mistralai/Mixtral-8x7B-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mixtral-8x7B-v0.1",
+        },
+        "Mixtral-8x22B-v0.1": {
+            DownloadSource.DEFAULT: "mistralai/Mixtral-8x22B-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mixtral-8x22B-v0.1",
+        },
+        "Mixtral-8x7B-v0.1-Instruct": {
+            DownloadSource.DEFAULT: "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mixtral-8x7B-Instruct-v0.1",
+        },
+        "Mixtral-8x22B-v0.1-Instruct": {
+            DownloadSource.DEFAULT: "mistralai/Mixtral-8x22B-Instruct-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mixtral-8x22B-Instruct-v0.1",
+        },
+    },
+    template="mistral",
+)
+
+
+register_model_group(
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+            DownloadSource.DEFAULT: "moonshotai/Moonlight-16B-A3B",
+            DownloadSource.MODELSCOPE: "moonshotai/Moonlight-16B-A3B",
+        },
+        "Moonlight-16B-A3B-Instruct": {
+            DownloadSource.DEFAULT: "moonshotai/Moonlight-16B-A3B-Instruct",
+            DownloadSource.MODELSCOPE: "moonshotai/Moonlight-16B-A3B-Instruct",
+        },
+    },
+    template="moonlight",
+)
+
+
+register_model_group(
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+            DownloadSource.DEFAULT: "allenai/OLMo-1B-hf",
+        },
+        "OLMo-7B": {
+            DownloadSource.DEFAULT: "allenai/OLMo-7B-hf",
+        },
+        "OLMo-7B-Chat": {
+            DownloadSource.DEFAULT: "ssec-uw/OLMo-7B-Instruct-hf",
+        },
+        "OLMo-1.7-7B": {
+            DownloadSource.DEFAULT: "allenai/OLMo-1.7-7B-hf",
+        },
+    },
+)
+
+
+register_model_group(
+    models={
+        "OpenChat3.5-7B-Chat": {
+            DownloadSource.DEFAULT: "openchat/openchat-3.5-0106",
+            DownloadSource.MODELSCOPE: "xcwzxcwz/openchat-3.5-0106",
+        }
+    },
+    template="openchat",
+)
+
+
+register_model_group(
+    models={
+        "OpenChat3.6-8B-Chat": {
+            DownloadSource.DEFAULT: "openchat/openchat-3.6-8b-20240522",
+        }
+    },
+    template="openchat-3.6",
+)
+
+
+register_model_group(
+    models={
+        "OpenCoder-1.5B-Base": {
+            DownloadSource.DEFAULT: "infly/OpenCoder-1.5B-Base",
+            DownloadSource.MODELSCOPE: "infly/OpenCoder-1.5B-Base",
+        },
+        "OpenCoder-8B-Base": {
+            DownloadSource.DEFAULT: "infly/OpenCoder-8B-Base",
+            DownloadSource.MODELSCOPE: "infly/OpenCoder-8B-Base",
+        },
+        "OpenCoder-1.5B-Instruct": {
+            DownloadSource.DEFAULT: "infly/OpenCoder-1.5B-Instruct",
+            DownloadSource.MODELSCOPE: "infly/OpenCoder-1.5B-Instruct",
+        },
+        "OpenCoder-8B-Instruct": {
+            DownloadSource.DEFAULT: "infly/OpenCoder-8B-Instruct",
+            DownloadSource.MODELSCOPE: "infly/OpenCoder-8B-Instruct",
+        },
+    },
+    template="opencoder",
+)
+
+
+register_model_group(
+    models={
+        "Orion-14B-Base": {
+            DownloadSource.DEFAULT: "OrionStarAI/Orion-14B-Base",
+            DownloadSource.MODELSCOPE: "OrionStarAI/Orion-14B-Base",
+        },
+        "Orion-14B-Chat": {
+            DownloadSource.DEFAULT: "OrionStarAI/Orion-14B-Chat",
+            DownloadSource.MODELSCOPE: "OrionStarAI/Orion-14B-Chat",
+        },
+        "Orion-14B-Long-Chat": {
+            DownloadSource.DEFAULT: "OrionStarAI/Orion-14B-LongChat",
+            DownloadSource.MODELSCOPE: "OrionStarAI/Orion-14B-LongChat",
+        },
+        "Orion-14B-RAG-Chat": {
+            DownloadSource.DEFAULT: "OrionStarAI/Orion-14B-Chat-RAG",
+            DownloadSource.MODELSCOPE: "OrionStarAI/Orion-14B-Chat-RAG",
+        },
+        "Orion-14B-Plugin-Chat": {
+            DownloadSource.DEFAULT: "OrionStarAI/Orion-14B-Chat-Plugin",
+            DownloadSource.MODELSCOPE: "OrionStarAI/Orion-14B-Chat-Plugin",
+        },
+    },
+    template="orion",
+)
+
+
+register_model_group(
+    models={
+        "PaliGemma-3B-pt-224": {
+            DownloadSource.DEFAULT: "google/paligemma-3b-pt-224",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma-3b-pt-224",
+        },
+        "PaliGemma-3B-pt-448": {
+            DownloadSource.DEFAULT: "google/paligemma-3b-pt-448",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma-3b-pt-448",
+        },
+        "PaliGemma-3B-pt-896": {
+            DownloadSource.DEFAULT: "google/paligemma-3b-pt-896",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma-3b-pt-896",
+        },
+        "PaliGemma-3B-mix-224": {
+            DownloadSource.DEFAULT: "google/paligemma-3b-mix-224",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma-3b-mix-224",
+        },
+        "PaliGemma-3B-mix-448": {
+            DownloadSource.DEFAULT: "google/paligemma-3b-mix-448",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma-3b-mix-448",
+        },
+    },
+    template="paligemma",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "PaliGemma2-3B-pt-224": {
+            DownloadSource.DEFAULT: "google/paligemma2-3b-pt-224",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-3b-pt-224",
+        },
+        "PaliGemma2-3B-pt-448": {
+            DownloadSource.DEFAULT: "google/paligemma2-3b-pt-448",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-3b-pt-448",
+        },
+        "PaliGemma2-3B-pt-896": {
+            DownloadSource.DEFAULT: "google/paligemma2-3b-pt-896",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-3b-pt-896",
+        },
+        "PaliGemma2-10B-pt-224": {
+            DownloadSource.DEFAULT: "google/paligemma2-10b-pt-224",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-10b-pt-224",
+        },
+        "PaliGemma2-10B-pt-448": {
+            DownloadSource.DEFAULT: "google/paligemma2-10b-pt-448",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-10b-pt-448",
+        },
+        "PaliGemma2-10B-pt-896": {
+            DownloadSource.DEFAULT: "google/paligemma2-10b-pt-896",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-10b-pt-896",
+        },
+        "PaliGemma2-28B-pt-224": {
+            DownloadSource.DEFAULT: "google/paligemma2-28b-pt-224",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-28b-pt-224",
+        },
+        "PaliGemma2-28B-pt-448": {
+            DownloadSource.DEFAULT: "google/paligemma2-28b-pt-448",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-28b-pt-448",
+        },
+        "PaliGemma2-28B-pt-896": {
+            DownloadSource.DEFAULT: "google/paligemma2-28b-pt-896",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/paligemma2-28b-pt-896",
+        },
+        "PaliGemma2-3B-mix-224": {
+            DownloadSource.DEFAULT: "google/paligemma2-3b-mix-224",
+            DownloadSource.MODELSCOPE: "mlx-community/paligemma2-3b-mix-224-bf16",
+        },
+        "PaliGemma2-3B-mix-448": {
+            DownloadSource.DEFAULT: "google/paligemma2-3b-mix-448",
+            DownloadSource.MODELSCOPE: "mlx-community/paligemma2-3b-mix-448-bf16",
+        },
+        "PaliGemma2-10B-mix-224": {
+            DownloadSource.DEFAULT: "google/paligemma2-10b-mix-224",
+            DownloadSource.MODELSCOPE: "mlx-community/paligemma2-10b-mix-224-bf16",
+        },
+        "PaliGemma2-10B-mix-448": {
+            DownloadSource.DEFAULT: "google/paligemma2-10b-mix-448",
+            DownloadSource.MODELSCOPE: "mlx-community/paligemma2-10b-mix-448-bf16",
+        },
+        "PaliGemma2-28B-mix-224": {
+            DownloadSource.DEFAULT: "google/paligemma2-28b-mix-224",
+            DownloadSource.MODELSCOPE: "mlx-community/paligemma2-28b-mix-224-bf16",
+        },
+        "PaliGemma2-28B-mix-448": {
+            DownloadSource.DEFAULT: "google/paligemma2-28b-mix-448",
+            DownloadSource.MODELSCOPE: "mlx-community/paligemma2-28b-mix-448-bf16",
+        },
+    },
+    template="paligemma",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Phi-1.5-1.3B": {
+            DownloadSource.DEFAULT: "microsoft/phi-1_5",
+            DownloadSource.MODELSCOPE: "allspace/PHI_1-5",
+        },
+        "Phi-2-2.7B": {
+            DownloadSource.DEFAULT: "microsoft/phi-2",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/phi-2",
+        },
+    }
+)
+
+
+register_model_group(
+    models={
+        "Phi-3-4B-4k-Instruct": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3-mini-4k-instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-mini-4k-instruct",
+        },
+        "Phi-3-4B-128k-Instruct": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3-mini-128k-instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-mini-128k-instruct",
+        },
+        "Phi-3-14B-8k-Instruct": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3-medium-4k-instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-medium-4k-instruct",
+        },
+        "Phi-3-14B-128k-Instruct": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3-medium-128k-instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-medium-128k-instruct",
+        },
+        "Phi-3.5-4B-instruct": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3.5-mini-instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3.5-mini-instruct",
+        },
+        "Phi-3.5-MoE-42B-A6.6B-instruct": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3.5-MoE-instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3.5-MoE-instruct",
+        },
+    },
+    template="phi",
+)
+
+
+register_model_group(
+    models={
+        "Phi-3-7B-8k-Instruct": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3-small-8k-instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-small-8k-instruct",
+        },
+        "Phi-3-7B-128k-Instruct": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3-small-128k-instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-small-128k-instruct",
+        },
+    },
+    template="phi_small",
+)
+
+
+register_model_group(
+    models={
+        "Phi-4-14B-Instruct": {
+            DownloadSource.DEFAULT: "microsoft/phi-4",
+            DownloadSource.MODELSCOPE: "LLM-Research/phi-4",
+        },
+    },
+    template="phi4",
+)
+
+
+register_model_group(
+    models={
+        "Pixtral-12B": {
+            DownloadSource.DEFAULT: "mistral-community/pixtral-12b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/pixtral-12b",
+        }
+    },
+    template="pixtral",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Qwen-1.8B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-1_8B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-1_8B",
+        },
+        "Qwen-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-7B",
+        },
+        "Qwen-14B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-14B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-14B",
+        },
+        "Qwen-72B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-72B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-72B",
+        },
+        "Qwen-1.8B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-1_8B-Chat",
+        },
+        "Qwen-7B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-7B-Chat",
+        },
+        "Qwen-14B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-14B-Chat",
+        },
+        "Qwen-72B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-72B-Chat",
+        },
+        "Qwen-1.8B-Chat-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-1_8B-Chat-Int8",
+        },
+        "Qwen-1.8B-Chat-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-1_8B-Chat-Int4",
+        },
+        "Qwen-7B-Chat-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-7B-Chat-Int8",
+        },
+        "Qwen-7B-Chat-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-7B-Chat-Int4",
+        },
+        "Qwen-14B-Chat-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-14B-Chat-Int8",
+        },
+        "Qwen-14B-Chat-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-14B-Chat-Int4",
+        },
+        "Qwen-72B-Chat-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-72B-Chat-Int8",
+        },
+        "Qwen-72B-Chat-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen-72B-Chat-Int4",
+        },
+    },
+    template="qwen",
+)
+
+
+register_model_group(
+    models={
+        "Qwen1.5-0.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-0.5B",
+        },
+        "Qwen1.5-1.8B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-1.8B",
+        },
+        "Qwen1.5-4B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-4B",
+        },
+        "Qwen1.5-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-7B",
+        },
+        "Qwen1.5-14B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-14B",
+        },
+        "Qwen1.5-32B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-32B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-32B",
+        },
+        "Qwen1.5-72B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-72B",
+        },
+        "Qwen1.5-110B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-110B",
+        },
+        "Qwen1.5-MoE-A2.7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-MoE-A2.7B",
+        },
+        "Qwen1.5-0.5B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-0.5B-Chat",
+        },
+        "Qwen1.5-1.8B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-1.8B-Chat",
+        },
+        "Qwen1.5-4B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-4B-Chat",
+        },
+        "Qwen1.5-7B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-7B-Chat",
+        },
+        "Qwen1.5-14B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-14B-Chat",
+        },
+        "Qwen1.5-32B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-32B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-32B-Chat",
+        },
+        "Qwen1.5-72B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-72B-Chat",
+        },
+        "Qwen1.5-110B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-110B-Chat",
+        },
+        "Qwen1.5-MoE-A2.7B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-MoE-A2.7B-Chat",
+        },
+        "Qwen1.5-0.5B-Chat-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
+        },
+        "Qwen1.5-0.5B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-0.5B-Chat-AWQ",
+        },
+        "Qwen1.5-1.8B-Chat-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8",
+        },
+        "Qwen1.5-1.8B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-1.8B-Chat-AWQ",
+        },
+        "Qwen1.5-4B-Chat-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-4B-Chat-GPTQ-Int8",
+        },
+        "Qwen1.5-4B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-4B-Chat-AWQ",
+        },
+        "Qwen1.5-7B-Chat-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-7B-Chat-GPTQ-Int8",
+        },
+        "Qwen1.5-7B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-7B-Chat-AWQ",
+        },
+        "Qwen1.5-14B-Chat-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-14B-Chat-GPTQ-Int8",
+        },
+        "Qwen1.5-14B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-14B-Chat-AWQ",
+        },
+        "Qwen1.5-32B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-32B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-32B-Chat-AWQ",
+        },
+        "Qwen1.5-72B-Chat-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-72B-Chat-GPTQ-Int8",
+        },
+        "Qwen1.5-72B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-72B-Chat-AWQ",
+        },
+        "Qwen1.5-110B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-110B-Chat-AWQ",
+        },
+        "Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
+        },
+        "CodeQwen1.5-7B": {
+            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B",
+            DownloadSource.MODELSCOPE: "Qwen/CodeQwen1.5-7B",
+        },
+        "CodeQwen1.5-7B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat",
+            DownloadSource.MODELSCOPE: "Qwen/CodeQwen1.5-7B-Chat",
+        },
+        "CodeQwen1.5-7B-Chat-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/CodeQwen1.5-7B-Chat-AWQ",
+        },
+    },
+    template="qwen",
+)
+
+
+register_model_group(
+    models={
+        "Qwen2-0.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-0.5B",
+        },
+        "Qwen2-1.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-1.5B",
+        },
+        "Qwen2-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-7B",
+        },
+        "Qwen2-72B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-72B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-72B",
+        },
+        "Qwen2-MoE-57B-A14B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-57B-A14B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-57B-A14B",
+        },
+        "Qwen2-0.5B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-0.5B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-0.5B-Instruct",
+        },
+        "Qwen2-1.5B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-1.5B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-1.5B-Instruct",
+        },
+        "Qwen2-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-7B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-7B-Instruct",
+        },
+        "Qwen2-72B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-72B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-72B-Instruct",
+        },
+        "Qwen2-MoE-57B-A14B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-57B-A14B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-57B-A14B-Instruct",
+        },
+        "Qwen2-0.5B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-0.5B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2-0.5B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-0.5B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2-0.5B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-0.5B-Instruct-AWQ",
+        },
+        "Qwen2-1.5B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-1.5B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2-1.5B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-1.5B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2-1.5B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-1.5B-Instruct-AWQ",
+        },
+        "Qwen2-7B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-7B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-7B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2-7B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-7B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-7B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2-7B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-7B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-7B-Instruct-AWQ",
+        },
+        "Qwen2-72B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-72B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-72B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2-72B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-72B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-72B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2-72B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-72B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-72B-Instruct-AWQ",
+        },
+        "Qwen2-57B-A14B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2-Math-1.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Math-1.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Math-1.5B",
+        },
+        "Qwen2-Math-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Math-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Math-7B",
+        },
+        "Qwen2-Math-72B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Math-72B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Math-72B",
+        },
+        "Qwen2-Math-1.5B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Math-1.5B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Math-1.5B-Instruct",
+        },
+        "Qwen2-Math-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Math-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Math-7B-Instruct",
+        },
+        "Qwen2-Math-72B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Math-72B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Math-72B-Instruct",
+        },
+    },
+    template="qwen",
+)
+
+
+register_model_group(
+    models={
+        "Qwen2.5-0.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-0.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-0.5B",
+        },
+        "Qwen2.5-1.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-1.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-1.5B",
+        },
+        "Qwen2.5-3B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-3B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-3B",
+        },
+        "Qwen2.5-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-7B",
+        },
+        "Qwen2.5-14B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-14B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-14B",
+        },
+        "Qwen2.5-32B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-32B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-32B",
+        },
+        "Qwen2.5-72B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-72B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-72B",
+        },
+        "Qwen2.5-0.5B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-0.5B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-0.5B-Instruct",
+        },
+        "Qwen2.5-1.5B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-1.5B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-1.5B-Instruct",
+        },
+        "Qwen2.5-3B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-3B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-3B-Instruct",
+        },
+        "Qwen2.5-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-7B-Instruct",
+        },
+        "Qwen2.5-14B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-14B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-14B-Instruct",
+        },
+        "Qwen2.5-32B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-32B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-32B-Instruct",
+        },
+        "Qwen2.5-72B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-72B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-72B-Instruct",
+        },
+        "Qwen2.5-7B-Instruct-1M": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-7B-Instruct-1M",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-7B-Instruct-1M",
+        },
+        "Qwen2.5-14B-Instruct-1M": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-14B-Instruct-1M",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-14B-Instruct-1M",
+        },
+        "Qwen2.5-0.5B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2.5-0.5B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2.5-0.5B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-0.5B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-0.5B-Instruct-AWQ",
+        },
+        "Qwen2.5-1.5B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2.5-1.5B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2.5-1.5B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-1.5B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-1.5B-Instruct-AWQ",
+        },
+        "Qwen2.5-3B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-3B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-3B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2.5-3B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-3B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-3B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2.5-3B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-3B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-3B-Instruct-AWQ",
+        },
+        "Qwen2.5-7B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-7B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-7B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2.5-7B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2.5-7B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-7B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-7B-Instruct-AWQ",
+        },
+        "Qwen2.5-14B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-14B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-14B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2.5-14B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-14B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-14B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2.5-14B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-14B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-14B-Instruct-AWQ",
+        },
+        "Qwen2.5-32B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2.5-32B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-32B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-32B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2.5-32B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-32B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-32B-Instruct-AWQ",
+        },
+        "Qwen2.5-72B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2.5-72B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-72B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-72B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2.5-72B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-72B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-72B-Instruct-AWQ",
+        },
+        "Qwen2.5-Coder-0.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-0.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-0.5B",
+        },
+        "Qwen2.5-Coder-1.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-1.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-1.5B",
+        },
+        "Qwen2.5-Coder-3B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-3B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-3B",
+        },
+        "Qwen2.5-Coder-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-7B",
+        },
+        "Qwen2.5-Coder-14B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-14B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-14B",
+        },
+        "Qwen2.5-Coder-32B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-32B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-32B",
+        },
+        "Qwen2.5-Coder-0.5B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-0.5B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-0.5B-Instruct",
+        },
+        "Qwen2.5-Coder-1.5B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-1.5B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-1.5B-Instruct",
+        },
+        "Qwen2.5-Coder-3B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-3B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-3B-Instruct",
+        },
+        "Qwen2.5-Coder-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-7B-Instruct",
+        },
+        "Qwen2.5-Coder-14B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-14B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-14B-Instruct",
+        },
+        "Qwen2.5-Coder-32B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Coder-32B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-32B-Instruct",
+        },
+        "Qwen2.5-Math-1.5B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Math-1.5B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Math-1.5B",
+        },
+        "Qwen2.5-Math-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Math-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Math-7B",
+        },
+        "Qwen2.5-Math-72B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Math-72B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Math-72B",
+        },
+        "Qwen2.5-Math-1.5B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Math-1.5B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-1.5B-Instruct",
+        },
+        "Qwen2.5-Math-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Math-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-7B-Instruct",
+        },
+        "Qwen2.5-Math-72B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Math-72B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Coder-72B-Instruct",
+        },
+        "QwQ-32B-Preview-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/QwQ-32B-Preview",
+            DownloadSource.MODELSCOPE: "Qwen/QwQ-32B-Preview",
+        },
+        "QwQ-32B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/QwQ-32B",
+            DownloadSource.MODELSCOPE: "Qwen/QwQ-32B",
+        },
+    },
+    template="qwen",
+)
+
+
+register_model_group(
+    models={
+        "Qwen3-0.6B-Base": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-0.6B-Base",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-0.6B-Base",
+        },
+        "Qwen3-1.7B-Base": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-1.7B-Base",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-1.7B-Base",
+        },
+        "Qwen3-4B-Base": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-4B-Base",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-4B-Base",
+        },
+        "Qwen3-8B-Base": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-8B-Base",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-8B-Base",
+        },
+        "Qwen3-14B-Base": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-14B-Base",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-14B-Base",
+        },
+        "Qwen3-30B-A3B-Base": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-30B-A3B-Base",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-30B-A3B-Base",
+        },
+        "Qwen3-0.6B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-0.6B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-0.6B",
+        },
+        "Qwen3-1.7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-1.7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-1.7B",
+        },
+        "Qwen3-4B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-4B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-4B",
+        },
+        "Qwen3-8B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-8B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-8B",
+        },
+        "Qwen3-14B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-14B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-14B",
+        },
+        "Qwen3-32B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-32B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-32B",
+        },
+        "Qwen3-30B-A3B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-30B-A3B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-30B-A3B",
+        },
+        "Qwen3-235B-A22B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-235B-A22B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-235B-A22B",
+        },
+        "Qwen3-0.6B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-0.6B-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-0.6B-GPTQ-Int8",
+        },
+        "Qwen3-1.7B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-1.7B-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-1.7B-GPTQ-Int8",
+        },
+        "Qwen3-4B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-4B-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-4B-AWQ",
+        },
+        "Qwen3-8B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-8B-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-8B-AWQ",
+        },
+        "Qwen3-14B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-14B-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-14B-AWQ",
+        },
+        "Qwen3-32B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-32B-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-32B-AWQ",
+        },
+        "Qwen3-30B-A3B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-30B-A3B-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-30B-A3B-GPTQ-Int4",
+        },
+        "Qwen3-235B-A22B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen3-235B-A22B-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen3-235B-A22B-GPTQ-Int4",
+        },
+    },
+    template="qwen3",
+)
+
+
+register_model_group(
+    models={
+        "Qwen2-Audio-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Audio-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Audio-7B",
+        },
+        "Qwen2-Audio-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Audio-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Audio-7B-Instruct",
+        },
+    },
+    template="qwen2_audio",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Qwen2.5-Omni-3B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-3B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-3B",
+        },
+        "Qwen2.5-Omni-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B",
+        },
+        "Qwen2.5-Omni-7B-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B-GPTQ-Int4",
+        },
+        "Qwen2.5-Omni-7B-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B-AWQ",
+        },
+    },
+    template="qwen2_omni",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Qwen2-VL-2B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-2B",
+        },
+        "Qwen2-VL-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-7B",
+        },
+        "Qwen2-VL-72B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-72B",
+        },
+        "Qwen2-VL-2B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-2B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-2B-Instruct",
+        },
+        "Qwen2-VL-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-7B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-7B-Instruct",
+        },
+        "Qwen2-VL-72B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-72B-Instruct",
+        },
+        "Qwen2-VL-2B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2-VL-2B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2-VL-2B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-2B-Instruct-AWQ",
+        },
+        "Qwen2-VL-7B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2-VL-7B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2-VL-7B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-7B-Instruct-AWQ",
+        },
+        "Qwen2-VL-72B-Instruct-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int8",
+        },
+        "Qwen2-VL-72B-Instruct-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
+        },
+        "Qwen2-VL-72B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-VL-72B-Instruct-AWQ",
+        },
+        "QVQ-72B-Preview": {
+            DownloadSource.DEFAULT: "Qwen/QVQ-72B-Preview",
+            DownloadSource.MODELSCOPE: "Qwen/QVQ-72B-Preview",
+        },
+        "Qwen2.5-VL-3B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-3B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-3B-Instruct",
+        },
+        "Qwen2.5-VL-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-7B-Instruct",
+        },
+        "Qwen2.5-VL-32B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-32B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-32B-Instruct",
+        },
+        "Qwen2.5-VL-72B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-72B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-72B-Instruct",
+        },
+        "Qwen2.5-VL-3B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-3B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-3B-Instruct-AWQ",
+        },
+        "Qwen2.5-VL-7B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-7B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-7B-Instruct-AWQ",
+        },
+        "Qwen2.5-VL-72B-Instruct-AWQ": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2.5-VL-72B-Instruct-AWQ",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-VL-72B-Instruct-AWQ",
+        },
+    },
+    template="qwen2_vl",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Seed-Coder-8B-Base": {
+            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-Coder-8B-Base",
+        },
+        "Seed-Coder-8B-Instruct": {
+            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-Coder-8B-Instruct",
+        },
+        "Seed-Coder-8B-Instruct-Reasoning": {
+            DownloadSource.DEFAULT: "ByteDance-Seed/Seed-Coder-8B-Reasoning-bf16",
+        },
+    },
+    template="seed_coder",
+)
+
+
+register_model_group(
+    models={
+        "Skywork-13B-Base": {
+            DownloadSource.DEFAULT: "Skywork/Skywork-13B-base",
+            DownloadSource.MODELSCOPE: "skywork/Skywork-13B-base",
+        }
+    }
+)
+
+
+register_model_group(
+    models={
+        "Skywork-o1-Open-Llama-3.1-8B": {
+            DownloadSource.DEFAULT: "Skywork/Skywork-o1-Open-Llama-3.1-8B",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Skywork-o1-Open-Llama-3.1-8B",
+        }
+    },
+    template="skywork_o1",
+)
+
+
+register_model_group(
+    models={
+        "SmolLM-135M": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM-135M",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM-135M",
+        },
+        "SmolLM-360M": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM-360M",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM-360M",
+        },
+        "SmolLM-1.7B": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM-1.7B",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM-1.7B",
+        },
+        "SmolLM-135M-Instruct": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM-135M-Instruct",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM-135M-Instruct",
+        },
+        "SmolLM-360M-Instruct": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM-360M-Instruct",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM-360M-Instruct",
+        },
+        "SmolLM-1.7B-Instruct": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM-1.7B-Instruct",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM-1.7B-Instruct",
+        },
+    },
+    template="smollm",
+)
+
+
+register_model_group(
+    models={
+        "SmolLM2-135M": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM2-135M",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM2-135M",
+        },
+        "SmolLM2-360M": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM2-360M",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM2-360M",
+        },
+        "SmolLM2-1.7B": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM2-1.7B",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM2-1.7B",
+        },
+        "SmolLM2-135M-Instruct": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM2-135M-Instruct",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM2-135M-Instruct",
+        },
+        "SmolLM2-360M-Instruct": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM2-360M-Instruct",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM2-360M-Instruct",
+        },
+        "SmolLM2-1.7B-Instruct": {
+            DownloadSource.DEFAULT: "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+            DownloadSource.MODELSCOPE: "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+        },
+    },
+    template="smollm2",
+)
+
+
+register_model_group(
+    models={
+        "SOLAR-10.7B-v1.0": {
+            DownloadSource.DEFAULT: "upstage/SOLAR-10.7B-v1.0",
+        },
+        "SOLAR-10.7B-Instruct-v1.0": {
+            DownloadSource.DEFAULT: "upstage/SOLAR-10.7B-Instruct-v1.0",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/SOLAR-10.7B-Instruct-v1.0",
+        },
+    },
+    template="solar",
+)
+
+
+register_model_group(
+    models={
+        "StarCoder2-3B": {
+            DownloadSource.DEFAULT: "bigcode/starcoder2-3b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/starcoder2-3b",
+        },
+        "StarCoder2-7B": {
+            DownloadSource.DEFAULT: "bigcode/starcoder2-7b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/starcoder2-7b",
+        },
+        "StarCoder2-15B": {
+            DownloadSource.DEFAULT: "bigcode/starcoder2-15b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/starcoder2-15b",
+        },
+    }
+)
+
+
+register_model_group(
+    models={
+        "TeleChat-1B-Chat": {
+            DownloadSource.DEFAULT: "Tele-AI/TeleChat-1B",
+            DownloadSource.MODELSCOPE: "TeleAI/TeleChat-1B",
+        },
+        "TeleChat-7B-Chat": {
+            DownloadSource.DEFAULT: "Tele-AI/telechat-7B",
+            DownloadSource.MODELSCOPE: "TeleAI/telechat-7B",
+            DownloadSource.OPENMIND: "TeleAI/TeleChat-7B-pt",
+        },
+        "TeleChat-12B-Chat": {
+            DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B-v2",
+            DownloadSource.MODELSCOPE: "TeleAI/TeleChat-12B-v2",
+            DownloadSource.OPENMIND: "TeleAI/TeleChat-12B-pt",
+        },
+        "TeleChat-52B-Chat": {
+            DownloadSource.DEFAULT: "Tele-AI/TeleChat-52B",
+        },
+    },
+    template="telechat",
+)
+
+
+register_model_group(
+    models={
+        "TeleChat2-3B-Chat": {
+            DownloadSource.DEFAULT: "Tele-AI/TeleChat2-3B",
+            DownloadSource.MODELSCOPE: "TeleAI/TeleChat2-3B",
+        },
+        "TeleChat2-7B-Chat": {
+            DownloadSource.DEFAULT: "Tele-AI/TeleChat2-7B",
+            DownloadSource.MODELSCOPE: "TeleAI/TeleChat2-7B",
+        },
+        "TeleChat2-35B-Chat": {
+            DownloadSource.MODELSCOPE: "TeleAI/TeleChat2-35B-Nov",
+        },
+        "TeleChat2-115B-Chat": {
+            DownloadSource.DEFAULT: "Tele-AI/TeleChat2-115B",
+            DownloadSource.MODELSCOPE: "TeleAI/TeleChat2-115B",
+        },
+    },
+    template="telechat2",
+)
+
+
+register_model_group(
+    models={
+        "Vicuna-v1.5-7B-Chat": {
+            DownloadSource.DEFAULT: "lmsys/vicuna-7b-v1.5",
+            DownloadSource.MODELSCOPE: "Xorbits/vicuna-7b-v1.5",
+        },
+        "Vicuna-v1.5-13B-Chat": {
+            DownloadSource.DEFAULT: "lmsys/vicuna-13b-v1.5",
+            DownloadSource.MODELSCOPE: "Xorbits/vicuna-13b-v1.5",
+        },
+    },
+    template="vicuna",
+)
+
+
+register_model_group(
+    models={
+        "Video-LLaVA-7B-Chat": {
+            DownloadSource.DEFAULT: "LanguageBind/Video-LLaVA-7B-hf",
+        },
+    },
+    template="video_llava",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "XuanYuan-6B": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B",
+        },
+        "XuanYuan-70B": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B",
+        },
+        "XuanYuan2-70B": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B",
+        },
+        "XuanYuan-6B-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat",
+        },
+        "XuanYuan-70B-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat",
+        },
+        "XuanYuan2-70B-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat",
+        },
+        "XuanYuan-6B-Chat-8bit": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
+        },
+        "XuanYuan-6B-Chat-4bit": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
+        },
+        "XuanYuan-70B-Chat-8bit": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
+        },
+        "XuanYuan-70B-Chat-4bit": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
+        },
+        "XuanYuan2-70B-Chat-8bit": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
+        },
+        "XuanYuan2-70B-Chat-4bit": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
+        },
+    },
+    template="xuanyuan",
+)
+
+
+register_model_group(
+    models={
+        "XVERSE-7B": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-7B",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-7B",
+        },
+        "XVERSE-13B": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-13B",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-13B",
+        },
+        "XVERSE-65B": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-65B",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-65B",
+        },
+        "XVERSE-65B-2": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-65B-2",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-65B-2",
+        },
+        "XVERSE-7B-Chat": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-7B-Chat",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-7B-Chat",
+        },
+        "XVERSE-13B-Chat": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-13B-Chat",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-13B-Chat",
+        },
+        "XVERSE-65B-Chat": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-65B-Chat",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-65B-Chat",
+        },
+        "XVERSE-MoE-A4.2B": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-MoE-A4.2B",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-MoE-A4.2B",
+        },
+        "XVERSE-7B-Chat-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-7B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-7B-Chat-GPTQ-Int8",
+        },
+        "XVERSE-7B-Chat-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-7B-Chat-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-7B-Chat-GPTQ-Int4",
+        },
+        "XVERSE-13B-Chat-GPTQ-Int8": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-13B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-13B-Chat-GPTQ-Int8",
+        },
+        "XVERSE-13B-Chat-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-13B-Chat-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-13B-Chat-GPTQ-Int4",
+        },
+        "XVERSE-65B-Chat-GPTQ-Int4": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-65B-Chat-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-65B-Chat-GPTQ-Int4",
+        },
+    },
+    template="xverse",
+)
+
+
+register_model_group(
+    models={
+        "Yayi-7B": {
+            DownloadSource.DEFAULT: "wenge-research/yayi-7b-llama2",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/yayi-7b-llama2",
+        },
+        "Yayi-13B": {
+            DownloadSource.DEFAULT: "wenge-research/yayi-13b-llama2",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/yayi-13b-llama2",
+        },
+    },
+    template="yayi",
+)
+
+
+register_model_group(
+    models={
+        "Yi-6B": {
+            DownloadSource.DEFAULT: "01-ai/Yi-6B",
+            DownloadSource.MODELSCOPE: "01ai/Yi-6B",
+        },
+        "Yi-9B": {
+            DownloadSource.DEFAULT: "01-ai/Yi-9B",
+            DownloadSource.MODELSCOPE: "01ai/Yi-9B",
+        },
+        "Yi-34B": {
+            DownloadSource.DEFAULT: "01-ai/Yi-34B",
+            DownloadSource.MODELSCOPE: "01ai/Yi-34B",
+        },
+        "Yi-6B-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat",
+            DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat",
+        },
+        "Yi-34B-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat",
+            DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat",
+        },
+        "Yi-6B-Chat-8bits": {
+            DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-8bits",
+            DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-8bits",
+        },
+        "Yi-6B-Chat-4bits": {
+            DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-4bits",
+            DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-4bits",
+        },
+        "Yi-34B-Chat-8bits": {
+            DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-8bits",
+            DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-8bits",
+        },
+        "Yi-34B-Chat-4bits": {
+            DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-4bits",
+            DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-4bits",
+        },
+        "Yi-1.5-6B": {
+            DownloadSource.DEFAULT: "01-ai/Yi-1.5-6B",
+            DownloadSource.MODELSCOPE: "01ai/Yi-1.5-6B",
+        },
+        "Yi-1.5-9B": {
+            DownloadSource.DEFAULT: "01-ai/Yi-1.5-9B",
+            DownloadSource.MODELSCOPE: "01ai/Yi-1.5-9B",
+        },
+        "Yi-1.5-34B": {
+            DownloadSource.DEFAULT: "01-ai/Yi-1.5-34B",
+            DownloadSource.MODELSCOPE: "01ai/Yi-1.5-34B",
+        },
+        "Yi-1.5-6B-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-1.5-6B-Chat",
+            DownloadSource.MODELSCOPE: "01ai/Yi-1.5-6B-Chat",
+            DownloadSource.OPENMIND: "LlamaFactory/Yi-1.5-6B-Chat",
+        },
+        "Yi-1.5-9B-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-1.5-9B-Chat",
+            DownloadSource.MODELSCOPE: "01ai/Yi-1.5-9B-Chat",
+        },
+        "Yi-1.5-34B-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-1.5-34B-Chat",
+            DownloadSource.MODELSCOPE: "01ai/Yi-1.5-34B-Chat",
+        },
+        "Yi-Coder-1.5B": {
+            DownloadSource.DEFAULT: "01-ai/Yi-Coder-1.5B",
+            DownloadSource.MODELSCOPE: "01ai/Yi-Coder-1.5B",
+        },
+        "Yi-Coder-9B": {
+            DownloadSource.DEFAULT: "01-ai/Yi-Coder-9B",
+            DownloadSource.MODELSCOPE: "01ai/Yi-Coder-9B",
+        },
+        "Yi-Coder-1.5B-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-Coder-1.5B-Chat",
+            DownloadSource.MODELSCOPE: "01ai/Yi-Coder-1.5B-Chat",
+        },
+        "Yi-Coder-9B-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-Coder-9B-Chat",
+            DownloadSource.MODELSCOPE: "01ai/Yi-Coder-9B-Chat",
+        },
+    },
+    template="yi",
+)
+
+
+register_model_group(
+    models={
+        "Yi-VL-6B-Chat": {
+            DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-6B-hf",
+        },
+        "Yi-VL-34B-Chat": {
+            DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-34B-hf",
+        },
+    },
+    template="yi_vl",
+    multimodal=True,
+)
+
+
+register_model_group(
+    models={
+        "Yuan2-2B-Chat": {
+            DownloadSource.DEFAULT: "IEITYuan/Yuan2-2B-hf",
+            DownloadSource.MODELSCOPE: "YuanLLM/Yuan2.0-2B-hf",
+        },
+        "Yuan2-51B-Chat": {
+            DownloadSource.DEFAULT: "IEITYuan/Yuan2-51B-hf",
+            DownloadSource.MODELSCOPE: "YuanLLM/Yuan2.0-51B-hf",
+        },
+        "Yuan2-102B-Chat": {
+            DownloadSource.DEFAULT: "IEITYuan/Yuan2-102B-hf",
+            DownloadSource.MODELSCOPE: "YuanLLM/Yuan2.0-102B-hf",
+        },
+    },
+    template="yuan",
+)
+
+
+register_model_group(
+    models={
+        "Zephyr-7B-Alpha-Chat": {
+            DownloadSource.DEFAULT: "HuggingFaceH4/zephyr-7b-alpha",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/zephyr-7b-alpha",
+        },
+        "Zephyr-7B-Beta-Chat": {
+            DownloadSource.DEFAULT: "HuggingFaceH4/zephyr-7b-beta",
+            DownloadSource.MODELSCOPE: "modelscope/zephyr-7b-beta",
+        },
+        "Zephyr-141B-ORPO-Chat": {
+            DownloadSource.DEFAULT: "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
+        },
+    },
+    template="zephyr",
+)

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/extras/env.py

+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/commands/env.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import platform
+
+import accelerate
+import datasets
+import peft
+import torch
+import transformers
+import trl
+from transformers.utils import is_torch_cuda_available, is_torch_npu_available
+
+
+VERSION = "0.9.4.dev0"
+
+
+def print_env() -> None:
+    info = {
+        "`llamafactory` version": VERSION,
+        "Platform": platform.platform(),
+        "Python version": platform.python_version(),
+        "PyTorch version": torch.__version__,
+        "Transformers version": transformers.__version__,
+        "Datasets version": datasets.__version__,
+        "Accelerate version": accelerate.__version__,
+        "PEFT version": peft.__version__,
+        "TRL version": trl.__version__,
+    }
+
+    if is_torch_cuda_available():
+        info["PyTorch version"] += " (GPU)"
+        info["GPU type"] = torch.cuda.get_device_name()
+        info["GPU number"] = torch.cuda.device_count()
+        info["GPU memory"] = f"{torch.cuda.mem_get_info()[1] / (1024**3):.2f}GB"
+
+    if is_torch_npu_available():
+        info["PyTorch version"] += " (NPU)"
+        info["NPU type"] = torch.npu.get_device_name()
+        info["CANN version"] = torch.version.cann
+
+    try:
+        import deepspeed  # type: ignore
+
+        info["DeepSpeed version"] = deepspeed.__version__
+    except Exception:
+        pass
+
+    try:
+        import bitsandbytes  # type: ignore
+
+        info["Bitsandbytes version"] = bitsandbytes.__version__
+    except Exception:
+        pass
+
+    try:
+        import vllm
+
+        info["vLLM version"] = vllm.__version__
+    except Exception:
+        pass
+
+    try:
+        import subprocess
+
+        commit_info = subprocess.run(["git", "rev-parse", "HEAD"], capture_output=True, text=True, check=True)
+        commit_hash = commit_info.stdout.strip()
+        info["Git commit"] = commit_hash
+    except Exception:
+        pass
+
+    if os.path.exists("data"):
+        info["Default data directory"] = "detected"
+    else:
+        info["Default data directory"] = "not detected"
+
+    print("\n" + "\n".join([f"- {key}: {value}" for key, value in info.items()]) + "\n")

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/extras/logging.py

+# Copyright 2025 Optuna, HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/logging.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import sys
+import threading
+from concurrent.futures import ThreadPoolExecutor
+from functools import lru_cache
+from typing import Optional
+
+from .constants import RUNNING_LOG
+
+
+_thread_lock = threading.RLock()
+_default_handler: Optional["logging.Handler"] = None
+_default_log_level: "logging._Level" = logging.INFO
+
+
+class LoggerHandler(logging.Handler):
+    r"""Redirect the logging output to the logging file for LLaMA Board."""
+
+    def __init__(self, output_dir: str) -> None:
+        super().__init__()
+        self._formatter = logging.Formatter(
+            fmt="[%(levelname)s|%(asctime)s] %(filename)s:%(lineno)s >> %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+        )
+        self.setLevel(logging.INFO)
+        os.makedirs(output_dir, exist_ok=True)
+        self.running_log = os.path.join(output_dir, RUNNING_LOG)
+        if os.path.exists(self.running_log):
+            os.remove(self.running_log)
+
+        self.thread_pool = ThreadPoolExecutor(max_workers=1)
+
+    def _write_log(self, log_entry: str) -> None:
+        with open(self.running_log, "a", encoding="utf-8") as f:
+            f.write(log_entry + "\n")
+
+    def emit(self, record) -> None:
+        if record.name == "httpx":
+            return
+
+        log_entry = self._formatter.format(record)
+        self.thread_pool.submit(self._write_log, log_entry)
+
+    def close(self) -> None:
+        self.thread_pool.shutdown(wait=True)
+        return super().close()
+
+
+class _Logger(logging.Logger):
+    r"""A logger that supports rank0 logging."""
+
+    def info_rank0(self, *args, **kwargs) -> None:
+        self.info(*args, **kwargs)
+
+    def warning_rank0(self, *args, **kwargs) -> None:
+        self.warning(*args, **kwargs)
+
+    def warning_rank0_once(self, *args, **kwargs) -> None:
+        self.warning(*args, **kwargs)
+
+
+def _get_default_logging_level() -> "logging._Level":
+    r"""Return the default logging level."""
+    env_level_str = os.getenv("LLAMAFACTORY_VERBOSITY", None)
+    if env_level_str:
+        if env_level_str.upper() in logging._nameToLevel:
+            return logging._nameToLevel[env_level_str.upper()]
+        else:
+            raise ValueError(f"Unknown logging level: {env_level_str}.")
+
+    return _default_log_level
+
+
+def _get_library_name() -> str:
+    return __name__.split(".")[0]
+
+
+def _get_library_root_logger() -> "_Logger":
+    return logging.getLogger(_get_library_name())
+
+
+def _configure_library_root_logger() -> None:
+    r"""Configure root logger using a stdout stream handler with an explicit format."""
+    global _default_handler
+
+    with _thread_lock:
+        if _default_handler:  # already configured
+            return
+
+        formatter = logging.Formatter(
+            fmt="[%(levelname)s|%(asctime)s] %(name)s:%(lineno)s >> %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+        )
+        _default_handler = logging.StreamHandler(sys.stdout)
+        _default_handler.setFormatter(formatter)
+        library_root_logger = _get_library_root_logger()
+        library_root_logger.addHandler(_default_handler)
+        library_root_logger.setLevel(_get_default_logging_level())
+        library_root_logger.propagate = False
+
+
+def get_logger(name: Optional[str] = None) -> "_Logger":
+    r"""Return a logger with the specified name. It it not supposed to be accessed externally."""
+    if name is None:
+        name = _get_library_name()
+
+    _configure_library_root_logger()
+    return logging.getLogger(name)
+
+
+def add_handler(handler: "logging.Handler") -> None:
+    r"""Add a handler to the root logger."""
+    _configure_library_root_logger()
+    _get_library_root_logger().addHandler(handler)
+
+
+def remove_handler(handler: logging.Handler) -> None:
+    r"""Remove a handler to the root logger."""
+    _configure_library_root_logger()
+    _get_library_root_logger().removeHandler(handler)
+
+
+def info_rank0(self: "logging.Logger", *args, **kwargs) -> None:
+    if int(os.getenv("LOCAL_RANK", "0")) == 0:
+        self.info(*args, **kwargs)
+
+
+def warning_rank0(self: "logging.Logger", *args, **kwargs) -> None:
+    if int(os.getenv("LOCAL_RANK", "0")) == 0:
+        self.warning(*args, **kwargs)
+
+
+@lru_cache(None)
+def warning_rank0_once(self: "logging.Logger", *args, **kwargs) -> None:
+    if int(os.getenv("LOCAL_RANK", "0")) == 0:
+        self.warning(*args, **kwargs)
+
+
+logging.Logger.info_rank0 = info_rank0
+logging.Logger.warning_rank0 = warning_rank0
+logging.Logger.warning_rank0_once = warning_rank0_once

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/extras/misc.py

+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's PEFT library.
+# https://github.com/huggingface/peft/blob/v0.10.0/src/peft/peft_model.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import os
+import socket
+from typing import TYPE_CHECKING, Any, Literal, Union
+
+import torch
+import torch.distributed as dist
+import transformers.dynamic_module_utils
+from huggingface_hub.utils import WeakFileLock
+from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
+from transformers.dynamic_module_utils import get_relative_imports
+from transformers.utils import (
+    is_torch_bf16_gpu_available,
+    is_torch_cuda_available,
+    is_torch_mps_available,
+    is_torch_npu_available,
+    is_torch_xpu_available,
+)
+from transformers.utils.versions import require_version
+
+from . import logging
+
+
+_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
+try:
+    _is_bf16_available = is_torch_bf16_gpu_available() or (is_torch_npu_available() and torch.npu.is_bf16_supported())
+except Exception:
+    _is_bf16_available = False
+
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+    from ..hparams import ModelArguments
+
+
+logger = logging.get_logger(__name__)
+
+
+class AverageMeter:
+    r"""Compute and store the average and current value."""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+def check_version(requirement: str, mandatory: bool = False) -> None:
+    r"""Optionally check the package version."""
+    if is_env_enabled("DISABLE_VERSION_CHECK") and not mandatory:
+        logger.warning_rank0_once("Version checking has been disabled, may lead to unexpected behaviors.")
+        return
+
+    if "gptmodel" in requirement or "autoawq" in requirement:
+        pip_command = f"pip install {requirement} --no-build-isolation"
+    else:
+        pip_command = f"pip install {requirement}"
+
+    if mandatory:
+        hint = f"To fix: run `{pip_command}`."
+    else:
+        hint = f"To fix: run `{pip_command}` or set `DISABLE_VERSION_CHECK=1` to skip this check."
+
+    require_version(requirement, hint)
+
+
+def check_dependencies() -> None:
+    r"""Check the version of the required packages."""
+    check_version("transformers>=4.49.0,<=4.52.4,!=4.52.0")
+    check_version("datasets>=2.16.0,<=3.6.0")
+    check_version("accelerate>=1.3.0,<=1.7.0")
+    check_version("peft>=0.14.0,<=0.15.2")
+    check_version("trl>=0.8.6,<=0.9.6")
+
+
+def calculate_tps(dataset: list[dict[str, Any]], metrics: dict[str, float], stage: Literal["sft", "rm"]) -> float:
+    r"""Calculate effective tokens per second."""
+    effective_token_num = 0
+    for data in dataset:
+        if stage == "sft":
+            effective_token_num += len(data["input_ids"])
+        elif stage == "rm":
+            effective_token_num += len(data["chosen_input_ids"]) + len(data["rejected_input_ids"])
+
+    result = effective_token_num * metrics["epoch"] / metrics["train_runtime"]
+    return result / dist.get_world_size() if dist.is_initialized() else result
+
+
+def count_parameters(model: "torch.nn.Module") -> tuple[int, int]:
+    r"""Return the number of trainable parameters and number of all parameters in the model."""
+    trainable_params, all_param = 0, 0
+    for param in model.parameters():
+        num_params = param.numel()
+        # if using DS Zero 3 and the weights are initialized empty
+        if num_params == 0 and hasattr(param, "ds_numel"):
+            num_params = param.ds_numel
+
+        # Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by itemsize
+        if param.__class__.__name__ == "Params4bit":
+            if hasattr(param, "quant_storage") and hasattr(param.quant_storage, "itemsize"):
+                num_bytes = param.quant_storage.itemsize
+            elif hasattr(param, "element_size"):  # for older pytorch version
+                num_bytes = param.element_size()
+            else:
+                num_bytes = 1
+
+            num_params = num_params * 2 * num_bytes
+
+        all_param += num_params
+        if param.requires_grad:
+            trainable_params += num_params
+
+    return trainable_params, all_param
+
+
+def get_current_device() -> "torch.device":
+    r"""Get the current available device."""
+    if is_torch_xpu_available():
+        device = "xpu:{}".format(os.getenv("LOCAL_RANK", "0"))
+    elif is_torch_npu_available():
+        device = "npu:{}".format(os.getenv("LOCAL_RANK", "0"))
+    elif is_torch_mps_available():
+        device = "mps:{}".format(os.getenv("LOCAL_RANK", "0"))
+    elif is_torch_cuda_available():
+        device = "cuda:{}".format(os.getenv("LOCAL_RANK", "0"))
+    else:
+        device = "cpu"
+
+    return torch.device(device)
+
+
+def get_device_count() -> int:
+    r"""Get the number of available devices."""
+    if is_torch_xpu_available():
+        return torch.xpu.device_count()
+    elif is_torch_npu_available():
+        return torch.npu.device_count()
+    elif is_torch_mps_available():
+        return torch.mps.device_count()
+    elif is_torch_cuda_available():
+        return torch.cuda.device_count()
+    else:
+        return 0
+
+
+def get_logits_processor() -> "LogitsProcessorList":
+    r"""Get logits processor that removes NaN and Inf logits."""
+    logits_processor = LogitsProcessorList()
+    logits_processor.append(InfNanRemoveLogitsProcessor())
+    return logits_processor
+
+
+def get_current_memory() -> tuple[int, int]:
+    r"""Get the available and total memory for the current device (in Bytes)."""
+    if is_torch_xpu_available():
+        return torch.xpu.mem_get_info()
+    elif is_torch_npu_available():
+        return torch.npu.mem_get_info()
+    elif is_torch_mps_available():
+        return torch.mps.current_allocated_memory(), torch.mps.recommended_max_memory()
+    elif is_torch_cuda_available():
+        return torch.cuda.mem_get_info()
+    else:
+        return 0, -1
+
+
+def get_peak_memory() -> tuple[int, int]:
+    r"""Get the peak memory usage (allocated, reserved) for the current device (in Bytes)."""
+    if is_torch_xpu_available():
+        return torch.xpu.max_memory_allocated(), torch.xpu.max_memory_reserved()
+    elif is_torch_npu_available():
+        return torch.npu.max_memory_allocated(), torch.npu.max_memory_reserved()
+    elif is_torch_mps_available():
+        return torch.mps.current_allocated_memory(), -1
+    elif is_torch_cuda_available():
+        return torch.cuda.max_memory_allocated(), torch.cuda.max_memory_reserved()
+    else:
+        return 0, -1
+
+
+def has_tokenized_data(path: "os.PathLike") -> bool:
+    r"""Check if the path has a tokenized dataset."""
+    return os.path.isdir(path) and len(os.listdir(path)) > 0
+
+
+def infer_optim_dtype(model_dtype: "torch.dtype") -> "torch.dtype":
+    r"""Infer the optimal dtype according to the model_dtype and device compatibility."""
+    if _is_bf16_available and model_dtype == torch.bfloat16:
+        return torch.bfloat16
+    elif _is_fp16_available:
+        return torch.float16
+    else:
+        return torch.float32
+
+
+def is_accelerator_available() -> bool:
+    r"""Check if the accelerator is available."""
+    return (
+        is_torch_xpu_available() or is_torch_npu_available() or is_torch_mps_available() or is_torch_cuda_available()
+    )
+
+
+def is_env_enabled(env_var: str, default: str = "0") -> bool:
+    r"""Check if the environment variable is enabled."""
+    return os.getenv(env_var, default).lower() in ["true", "y", "1"]
+
+
+def numpify(inputs: Union["NDArray", "torch.Tensor"]) -> "NDArray":
+    r"""Cast a torch tensor or a numpy array to a numpy array."""
+    if isinstance(inputs, torch.Tensor):
+        inputs = inputs.cpu()
+        if inputs.dtype == torch.bfloat16:  # numpy does not support bfloat16 until 1.21.4
+            inputs = inputs.to(torch.float32)
+
+        inputs = inputs.numpy()
+
+    return inputs
+
+
+def skip_check_imports() -> None:
+    r"""Avoid flash attention import error in custom model files."""
+    if not is_env_enabled("FORCE_CHECK_IMPORTS"):
+        transformers.dynamic_module_utils.check_imports = get_relative_imports
+
+
+def torch_gc() -> None:
+    r"""Collect the device memory."""
+    gc.collect()
+    if is_torch_xpu_available():
+        torch.xpu.empty_cache()
+    elif is_torch_npu_available():
+        torch.npu.empty_cache()
+    elif is_torch_mps_available():
+        torch.mps.empty_cache()
+    elif is_torch_cuda_available():
+        torch.cuda.empty_cache()
+
+
+def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
+    if (not use_modelscope() and not use_openmind()) or os.path.exists(model_args.model_name_or_path):
+        return model_args.model_name_or_path
+
+    if use_modelscope():
+        check_version("modelscope>=1.14.0", mandatory=True)
+        from modelscope import snapshot_download  # type: ignore
+        from modelscope.hub.api import HubApi  # type: ignore
+
+        if model_args.ms_hub_token:
+            api = HubApi()
+            api.login(model_args.ms_hub_token)
+
+        revision = "master" if model_args.model_revision == "main" else model_args.model_revision
+        with WeakFileLock(os.path.abspath(os.path.expanduser("~/.cache/llamafactory/modelscope.lock"))):
+            model_path = snapshot_download(
+                model_args.model_name_or_path,
+                revision=revision,
+                cache_dir=model_args.cache_dir,
+            )
+
+        return model_path
+
+    if use_openmind():
+        check_version("openmind>=0.8.0", mandatory=True)
+        from openmind.utils.hub import snapshot_download  # type: ignore
+
+        with WeakFileLock(os.path.abspath(os.path.expanduser("~/.cache/llamafactory/openmind.lock"))):
+            model_path = snapshot_download(
+                model_args.model_name_or_path,
+                revision=model_args.model_revision,
+                cache_dir=model_args.cache_dir,
+            )
+
+        return model_path
+
+
+def use_modelscope() -> bool:
+    return is_env_enabled("USE_MODELSCOPE_HUB")
+
+
+def use_openmind() -> bool:
+    return is_env_enabled("USE_OPENMIND_HUB")
+
+
+def use_ray() -> bool:
+    return is_env_enabled("USE_RAY")
+
+
+def find_available_port() -> int:
+    r"""Find an available port on the local machine."""
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    sock.bind(("", 0))
+    port = sock.getsockname()[1]
+    sock.close()
+    return port
+
+
+def fix_proxy(ipv6_enabled: bool = False) -> None:
+    r"""Fix proxy settings for gradio ui."""
+    os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0"
+    if ipv6_enabled:
+        os.environ.pop("http_proxy", None)
+        os.environ.pop("HTTP_PROXY", None)

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/extras/packages.py

+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/import_utils.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import importlib.metadata
+import importlib.util
+from functools import lru_cache
+from typing import TYPE_CHECKING
+
+from packaging import version
+
+
+if TYPE_CHECKING:
+    from packaging.version import Version
+
+
+def _is_package_available(name: str) -> bool:
+    return importlib.util.find_spec(name) is not None
+
+
+def _get_package_version(name: str) -> "Version":
+    try:
+        return version.parse(importlib.metadata.version(name))
+    except Exception:
+        return version.parse("0.0.0")
+
+
+def is_pyav_available():
+    return _is_package_available("av")
+
+
+def is_librosa_available():
+    return _is_package_available("librosa")
+
+
+def is_fastapi_available():
+    return _is_package_available("fastapi")
+
+
+def is_galore_available():
+    return _is_package_available("galore_torch")
+
+
+def is_apollo_available():
+    return _is_package_available("apollo_torch")
+
+
+def is_gradio_available():
+    return _is_package_available("gradio")
+
+
+def is_matplotlib_available():
+    return _is_package_available("matplotlib")
+
+
+def is_pillow_available():
+    return _is_package_available("PIL")
+
+
+def is_ray_available():
+    return _is_package_available("ray")
+
+
+def is_requests_available():
+    return _is_package_available("requests")
+
+
+def is_rouge_available():
+    return _is_package_available("rouge_chinese")
+
+
+def is_starlette_available():
+    return _is_package_available("sse_starlette")
+
+
+@lru_cache
+def is_transformers_version_greater_than(content: str):
+    return _get_package_version("transformers") >= version.parse(content)
+
+
+def is_uvicorn_available():
+    return _is_package_available("uvicorn")
+
+
+def is_vllm_available():
+    return _is_package_available("vllm")
+
+
+def is_sglang_available():
+    return _is_package_available("sglang")

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/extras/ploting.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import math
+import os
+from typing import Any
+
+from transformers.trainer import TRAINER_STATE_NAME
+
+from . import logging
+from .packages import is_matplotlib_available
+
+
+if is_matplotlib_available():
+    import matplotlib.figure
+    import matplotlib.pyplot as plt
+
+
+logger = logging.get_logger(__name__)
+
+
+def smooth(scalars: list[float]) -> list[float]:
+    r"""EMA implementation according to TensorBoard."""
+    if len(scalars) == 0:
+        return []
+
+    last = scalars[0]
+    smoothed = []
+    weight = 1.8 * (1 / (1 + math.exp(-0.05 * len(scalars))) - 0.5)  # a sigmoid function
+    for next_val in scalars:
+        smoothed_val = last * weight + (1 - weight) * next_val
+        smoothed.append(smoothed_val)
+        last = smoothed_val
+    return smoothed
+
+
+def gen_loss_plot(trainer_log: list[dict[str, Any]]) -> "matplotlib.figure.Figure":
+    r"""Plot loss curves in LlamaBoard."""
+    plt.close("all")
+    plt.switch_backend("agg")
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    steps, losses = [], []
+    for log in trainer_log:
+        if log.get("loss", None):
+            steps.append(log["current_steps"])
+            losses.append(log["loss"])
+
+    ax.plot(steps, losses, color="#1f77b4", alpha=0.4, label="original")
+    ax.plot(steps, smooth(losses), color="#1f77b4", label="smoothed")
+    ax.legend()
+    ax.set_xlabel("step")
+    ax.set_ylabel("loss")
+    return fig
+
+
+def plot_loss(save_dictionary: str, keys: list[str] = ["loss"]) -> None:
+    r"""Plot loss curves and saves the image."""
+    plt.switch_backend("agg")
+    with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), encoding="utf-8") as f:
+        data = json.load(f)
+
+    for key in keys:
+        steps, metrics = [], []
+        for i in range(len(data["log_history"])):
+            if key in data["log_history"][i]:
+                steps.append(data["log_history"][i]["step"])
+                metrics.append(data["log_history"][i][key])
+
+        if len(metrics) == 0:
+            logger.warning_rank0(f"No metric {key} to plot.")
+            continue
+
+        plt.figure()
+        plt.plot(steps, metrics, color="#1f77b4", alpha=0.4, label="original")
+        plt.plot(steps, smooth(metrics), color="#1f77b4", label="smoothed")
+        plt.title(f"training {key} of {save_dictionary}")
+        plt.xlabel("step")
+        plt.ylabel(key)
+        plt.legend()
+        figure_path = os.path.join(save_dictionary, "training_{}.png".format(key.replace("/", "_")))
+        plt.savefig(figure_path, format="png", dpi=100)
+        print("Figure saved at:", figure_path)

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/hparams/__init__.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .data_args import DataArguments
+from .evaluation_args import EvaluationArguments
+from .finetuning_args import FinetuningArguments
+from .generating_args import GeneratingArguments
+from .model_args import ModelArguments
+from .parser import get_eval_args, get_infer_args, get_ray_args, get_train_args, read_args
+from .training_args import RayArguments, TrainingArguments
+
+
+__all__ = [
+    "DataArguments",
+    "EvaluationArguments",
+    "FinetuningArguments",
+    "GeneratingArguments",
+    "ModelArguments",
+    "RayArguments",
+    "TrainingArguments",
+    "get_eval_args",
+    "get_infer_args",
+    "get_ray_args",
+    "get_train_args",
+    "read_args",
+]

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/hparams/data_args.py

+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import asdict, dataclass, field
+from typing import Any, Literal, Optional
+
+
+@dataclass
+class DataArguments:
+    r"""Arguments pertaining to what data we are going to input our model for training and evaluation."""
+
+    template: Optional[str] = field(
+        default=None,
+        metadata={"help": "Which template to use for constructing prompts in training and inference."},
+    )
+    dataset: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of dataset(s) to use for training. Use commas to separate multiple datasets."},
+    )
+    eval_dataset: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets."},
+    )
+    dataset_dir: str = field(
+        default="data",
+        metadata={"help": "Path to the folder containing the datasets."},
+    )
+    media_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the folder containing the images, videos or audios. Defaults to `dataset_dir`."},
+    )
+    cutoff_len: int = field(
+        default=2048,
+        metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
+    )
+    train_on_prompt: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to disable the mask on the prompt."},
+    )
+    mask_history: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to mask the history and train on the last turn only."},
+    )
+    streaming: bool = field(
+        default=False,
+        metadata={"help": "Enable dataset streaming."},
+    )
+    buffer_size: int = field(
+        default=16384,
+        metadata={"help": "Size of the buffer to randomly sample examples from in dataset streaming."},
+    )
+    mix_strategy: Literal["concat", "interleave_under", "interleave_over"] = field(
+        default="concat",
+        metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."},
+    )
+    interleave_probs: Optional[str] = field(
+        default=None,
+        metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets."},
+    )
+    preprocessing_batch_size: int = field(
+        default=1000,
+        metadata={"help": "The number of examples in one group in pre-processing."},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the pre-processing."},
+    )
+    max_samples: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."},
+    )
+    eval_num_beams: Optional[int] = field(
+        default=None,
+        metadata={"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`"},
+    )
+    ignore_pad_token_for_loss: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to ignore the tokens corresponding to the pad label in loss computation."},
+    )
+    val_size: float = field(
+        default=0.0,
+        metadata={"help": "Size of the validation set, should be an integer or a float in range `[0,1)`."},
+    )
+    eval_on_each_dataset: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to evaluate on each dataset separately."},
+    )
+    packing: Optional[bool] = field(
+        default=None,
+        metadata={"help": "Enable sequences packing in training. Will automatically enable in pre-training."},
+    )
+    neat_packing: bool = field(
+        default=False,
+        metadata={"help": "Enable sequence packing without cross-attention."},
+    )
+    tool_format: Optional[str] = field(
+        default=None,
+        metadata={"help": "Tool format to use for constructing function calling examples."},
+    )
+    default_system: Optional[str] = field(
+        default=None,
+        metadata={"help": "Override the default system message in the template."},
+    )
+    enable_thinking: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether or not to enable thinking mode for reasoning models."},
+    )
+    tokenized_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Path to save or load the tokenized datasets. "
+                "If tokenized_path not exists, it will save the tokenized datasets. "
+                "If tokenized_path exists, it will load the tokenized datasets."
+            )
+        },
+    )
+    data_shared_file_system: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use a shared file system for the datasets."},
+    )
+
+    def __post_init__(self):
+        def split_arg(arg):
+            if isinstance(arg, str):
+                return [item.strip() for item in arg.split(",")]
+            return arg
+
+        self.dataset = split_arg(self.dataset)
+        self.eval_dataset = split_arg(self.eval_dataset)
+
+        if self.media_dir is None:
+            self.media_dir = self.dataset_dir
+
+        if self.dataset is None and self.val_size > 1e-6:
+            raise ValueError("Cannot specify `val_size` if `dataset` is None.")
+
+        if self.eval_dataset is not None and self.val_size > 1e-6:
+            raise ValueError("Cannot specify `val_size` if `eval_dataset` is not None.")
+
+        if self.interleave_probs is not None:
+            if self.mix_strategy == "concat":
+                raise ValueError("`interleave_probs` is only valid for interleaved mixing.")
+
+            self.interleave_probs = list(map(float, split_arg(self.interleave_probs)))
+            if self.dataset is not None and len(self.dataset) != len(self.interleave_probs):
+                raise ValueError("The length of dataset and interleave probs should be identical.")
+
+            if self.eval_dataset is not None and len(self.eval_dataset) != len(self.interleave_probs):
+                raise ValueError("The length of eval dataset and interleave probs should be identical.")
+
+        if self.streaming and self.val_size > 1e-6 and self.val_size < 1:
+            raise ValueError("Streaming mode should have an integer val size.")
+
+        if self.streaming and self.max_samples is not None:
+            raise ValueError("`max_samples` is incompatible with `streaming`.")
+
+        if self.mask_history and self.train_on_prompt:
+            raise ValueError("`mask_history` is incompatible with `train_on_prompt`.")
+
+        if self.neat_packing:
+            self.packing = True
+
+        if self.packing:
+            self.cutoff_len -= 1  # avoid pad_to_multiple_of, needs improve
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)

docker-hub/qwen2.5-vl/llama-factory/src/llamafactory/hparams/evaluation_args.py

+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from dataclasses import dataclass, field
+from typing import Literal, Optional
+
+from datasets import DownloadMode
+
+
+@dataclass
+class EvaluationArguments:
+    r"""Arguments pertaining to specify the evaluation parameters."""
+
+    task: str = field(
+        metadata={"help": "Name of the evaluation task."},
+    )
+    task_dir: str = field(
+        default="evaluation",
+        metadata={"help": "Path to the folder containing the evaluation datasets."},
+    )
+    batch_size: int = field(
+        default=4,
+        metadata={"help": "The batch size per GPU for evaluation."},
+    )
+    seed: int = field(
+        default=42,
+        metadata={"help": "Random seed to be used with data loaders."},
+    )
+    lang: Literal["en", "zh"] = field(
+        default="en",
+        metadata={"help": "Language used at evaluation."},
+    )
+    n_shot: int = field(
+        default=5,
+        metadata={"help": "Number of examplars for few-shot learning."},
+    )
+    save_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to save the evaluation results."},
+    )
+    download_mode: DownloadMode = field(
+        default=DownloadMode.REUSE_DATASET_IF_EXISTS,
+        metadata={"help": "Download mode used for the evaluation datasets."},
+    )
+
+    def __post_init__(self):
+        if self.save_dir is not None and os.path.exists(self.save_dir):
+            raise ValueError("`save_dir` already exists, use another one.")