add grpo

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()

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：",
+)

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,
+)
+
+
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+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-Z1-9B-0414",
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+            DownloadSource.MODELSCOPE: "ZhipuAI/GLM-Z1-32B-0414",
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+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "goodbai95/GPT2-xl",
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+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-2b-base",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-8b-base",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.0-2b-instruct",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-1b-a400m-instruct",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-3b-a800m-instruct",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-2b-instruct",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-8b-instruct",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.2-2b-instruct",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.2-8b-instruct",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-2b-instruct",
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-8b-instruct",
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+
+
+register_model_group(
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+
+
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+            DownloadSource.MODELSCOPE: "AI-ModelScope/Hunyuan-7B-Instruct",
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+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-32K",
+        },
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+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm-chat-7b",
+        },
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm-chat-20b",
+        },
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+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-7b",
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-chat-7b",
+        },
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-chat-20b",
+        },
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b",
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b",
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-20b",
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b-chat",
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat",
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat-1m",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat-1m",
+        },
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-20b-chat",
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+            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm3-8b-instruct",
+        },
+    },
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+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL2_5-8B-MPO-hf",
+        },
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+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-1B-hf",
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+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-2B-hf",
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+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-14B-hf",
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+        "InternVL3-38B-hf": {
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+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-38B-hf",
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+            DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-78B-hf",
+        },
+    },
+    template="intern_vl",
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+)
+
+
+register_model_group(
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+    },
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-Dev-72B",
+        },
+    },
+    template="qwen",
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Instruct",
+        },
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+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Thinking",
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+            DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Thinking-2506",
+        },
+    },
+    template="kimi_vl",
+    multimodal=True,
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "DeepLang/LingoWhale-8B",
+        }
+    },
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "skyline2006/llama-7b",
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+            DownloadSource.MODELSCOPE: "skyline2006/llama-13b",
+        },
+        "Llama-30B": {
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+            DownloadSource.MODELSCOPE: "skyline2006/llama-30b",
+        },
+        "Llama-65B": {
+            DownloadSource.DEFAULT: "huggyllama/llama-65b",
+            DownloadSource.MODELSCOPE: "skyline2006/llama-65b",
+        },
+    }
+)
+
+
+register_model_group(
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-ms",
+        },
+        "Llama-2-13B": {
+            DownloadSource.DEFAULT: "meta-llama/Llama-2-13b-hf",
+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-ms",
+        },
+        "Llama-2-70B": {
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-ms",
+        },
+        "Llama-2-7B-Chat": {
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-chat-ms",
+        },
+        "Llama-2-13B-Chat": {
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-chat-ms",
+        },
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+            DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-chat-ms",
+        },
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+)
+
+
+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",
+        },
+        "Llama-3-8B-Instruct": {
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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",
+        },
+        "Llama-3-8B-Chinese-Chat": {
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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",
+        },
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+            DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-8B-Chinese-Chat",
+        },
+        "Llama-3.1-70B-Chinese-Chat": {
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+            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": {
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+            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.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",
+)

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")

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

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)

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")

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)

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",
+]

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)

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.")

src/llamafactory/hparams/finetuning_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.
+
+from dataclasses import asdict, dataclass, field
+from typing import Any, Literal, Optional
+
+
+@dataclass
+class FreezeArguments:
+    r"""Arguments pertaining to the freeze (partial-parameter) training."""
+
+    freeze_trainable_layers: int = field(
+        default=2,
+        metadata={
+            "help": (
+                "The number of trainable layers for freeze (partial-parameter) fine-tuning. "
+                "Positive numbers mean the last n layers are set as trainable, "
+                "negative numbers mean the first n layers are set as trainable."
+            )
+        },
+    )
+    freeze_trainable_modules: str = field(
+        default="all",
+        metadata={
+            "help": (
+                "Name(s) of trainable modules for freeze (partial-parameter) fine-tuning. "
+                "Use commas to separate multiple modules. "
+                "Use `all` to specify all the available modules."
+            )
+        },
+    )
+    freeze_extra_modules: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Name(s) of modules apart from hidden layers to be set as trainable "
+                "for freeze (partial-parameter) fine-tuning. "
+                "Use commas to separate multiple modules."
+            )
+        },
+    )
+
+
+@dataclass
+class LoraArguments:
+    r"""Arguments pertaining to the LoRA training."""
+
+    additional_target: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Name(s) of modules apart from LoRA layers to be set as trainable "
+                "and saved in the final checkpoint. "
+                "Use commas to separate multiple modules."
+            )
+        },
+    )
+    lora_alpha: Optional[int] = field(
+        default=None,
+        metadata={"help": "The scale factor for LoRA fine-tuning (default: lora_rank * 2)."},
+    )
+    lora_dropout: float = field(
+        default=0.0,
+        metadata={"help": "Dropout rate for the LoRA fine-tuning."},
+    )
+    lora_rank: int = field(
+        default=8,
+        metadata={"help": "The intrinsic dimension for LoRA fine-tuning."},
+    )
+    lora_target: str = field(
+        default="all",
+        metadata={
+            "help": (
+                "Name(s) of target modules to apply LoRA. "
+                "Use commas to separate multiple modules. "
+                "Use `all` to specify all the linear modules."
+            )
+        },
+    )
+    loraplus_lr_ratio: Optional[float] = field(
+        default=None,
+        metadata={"help": "LoRA plus learning rate ratio (lr_B / lr_A)."},
+    )
+    loraplus_lr_embedding: float = field(
+        default=1e-6,
+        metadata={"help": "LoRA plus learning rate for lora embedding layers."},
+    )
+    use_rslora: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the rank stabilization scaling factor for LoRA layer."},
+    )
+    use_dora: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the weight-decomposed lora method (DoRA)."},
+    )
+    pissa_init: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to initialize a PiSSA adapter."},
+    )
+    pissa_iter: int = field(
+        default=16,
+        metadata={"help": "The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it."},
+    )
+    pissa_convert: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to convert the PiSSA adapter to a normal LoRA adapter."},
+    )
+    create_new_adapter: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to create a new adapter with randomly initialized weight."},
+    )
+
+
+@dataclass
+class RLHFArguments:
+    r"""Arguments pertaining to the PPO, DPO and KTO training."""
+
+    pref_beta: float = field(
+        default=0.1,
+        metadata={"help": "The beta parameter in the preference loss."},
+    )
+    pref_ftx: float = field(
+        default=0.0,
+        metadata={"help": "The supervised fine-tuning loss coefficient in DPO training."},
+    )
+    pref_loss: Literal["sigmoid", "hinge", "ipo", "kto_pair", "orpo", "simpo"] = field(
+        default="sigmoid",
+        metadata={"help": "The type of DPO loss to use."},
+    )
+    dpo_label_smoothing: float = field(
+        default=0.0,
+        metadata={"help": "The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5."},
+    )
+    kto_chosen_weight: float = field(
+        default=1.0,
+        metadata={"help": "The weight factor of the desirable losses in KTO training."},
+    )
+    kto_rejected_weight: float = field(
+        default=1.0,
+        metadata={"help": "The weight factor of the undesirable losses in KTO training."},
+    )
+    simpo_gamma: float = field(
+        default=0.5,
+        metadata={"help": "The target reward margin term in SimPO loss."},
+    )
+    ppo_buffer_size: int = field(
+        default=1,
+        metadata={"help": "The number of mini-batches to make experience buffer in a PPO optimization step."},
+    )
+    ppo_epochs: int = field(
+        default=4,
+        metadata={"help": "The number of epochs to perform in a PPO optimization step."},
+    )
+    ppo_score_norm: bool = field(
+        default=False,
+        metadata={"help": "Use score normalization in PPO training."},
+    )
+    ppo_target: float = field(
+        default=6.0,
+        metadata={"help": "Target KL value for adaptive KL control in PPO training."},
+    )
+    ppo_whiten_rewards: bool = field(
+        default=False,
+        metadata={"help": "Whiten the rewards before compute advantages in PPO training."},
+    )
+    ref_model: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the reference model used for the PPO or DPO training."},
+    )
+    ref_model_adapters: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the adapters of the reference model."},
+    )
+    ref_model_quantization_bit: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of bits to quantize the reference model."},
+    )
+    reward_model: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the reward model used for the PPO training."},
+    )
+    reward_model_adapters: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the adapters of the reward model."},
+    )
+    reward_model_quantization_bit: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of bits to quantize the reward model."},
+    )
+    reward_model_type: Literal["lora", "full", "api"] = field(
+        default="lora",
+        metadata={"help": "The type of the reward model in PPO training. Lora model only supports lora training."},
+    )
+    ld_alpha: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Alpha parameter from the LD-DPO paper, which controls the weighting of"
+                " the verbose token log-probabilities in responses."
+            )
+        },
+    )
+
+
+@dataclass
+class GaloreArguments:
+    r"""Arguments pertaining to the GaLore algorithm."""
+
+    use_galore: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the gradient low-Rank projection (GaLore)."},
+    )
+    galore_target: str = field(
+        default="all",
+        metadata={
+            "help": (
+                "Name(s) of modules to apply GaLore. Use commas to separate multiple modules. "
+                "Use `all` to specify all the linear modules."
+            )
+        },
+    )
+    galore_rank: int = field(
+        default=16,
+        metadata={"help": "The rank of GaLore gradients."},
+    )
+    galore_update_interval: int = field(
+        default=200,
+        metadata={"help": "Number of steps to update the GaLore projection."},
+    )
+    galore_scale: float = field(
+        default=2.0,
+        metadata={"help": "GaLore scaling coefficient."},
+    )
+    galore_proj_type: Literal["std", "reverse_std", "right", "left", "full"] = field(
+        default="std",
+        metadata={"help": "Type of GaLore projection."},
+    )
+    galore_layerwise: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to enable layer-wise update to further save memory."},
+    )
+
+
+@dataclass
+class ApolloArguments:
+    r"""Arguments pertaining to the APOLLO algorithm."""
+
+    use_apollo: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the APOLLO optimizer."},
+    )
+    apollo_target: str = field(
+        default="all",
+        metadata={
+            "help": (
+                "Name(s) of modules to apply APOLLO. Use commas to separate multiple modules. "
+                "Use `all` to specify all the linear modules."
+            )
+        },
+    )
+    apollo_rank: int = field(
+        default=16,
+        metadata={"help": "The rank of APOLLO gradients."},
+    )
+    apollo_update_interval: int = field(
+        default=200,
+        metadata={"help": "Number of steps to update the APOLLO projection."},
+    )
+    apollo_scale: float = field(
+        default=32.0,
+        metadata={"help": "APOLLO scaling coefficient."},
+    )
+    apollo_proj: Literal["svd", "random"] = field(
+        default="random",
+        metadata={"help": "Type of APOLLO low-rank projection algorithm (svd or random)."},
+    )
+    apollo_proj_type: Literal["std", "right", "left"] = field(
+        default="std",
+        metadata={"help": "Type of APOLLO projection."},
+    )
+    apollo_scale_type: Literal["channel", "tensor"] = field(
+        default="channel",
+        metadata={"help": "Type of APOLLO scaling (channel or tensor)."},
+    )
+    apollo_layerwise: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to enable layer-wise update to further save memory."},
+    )
+    apollo_scale_front: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the norm-growth limiter in front of gradient scaling."},
+    )
+
+
+@dataclass
+class BAdamArgument:
+    r"""Arguments pertaining to the BAdam optimizer."""
+
+    use_badam: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the BAdam optimizer."},
+    )
+    badam_mode: Literal["layer", "ratio"] = field(
+        default="layer",
+        metadata={"help": "Whether to use layer-wise or ratio-wise BAdam optimizer."},
+    )
+    badam_start_block: Optional[int] = field(
+        default=None,
+        metadata={"help": "The starting block index for layer-wise BAdam."},
+    )
+    badam_switch_mode: Optional[Literal["ascending", "descending", "random", "fixed"]] = field(
+        default="ascending",
+        metadata={"help": "the strategy of picking block to update for layer-wise BAdam."},
+    )
+    badam_switch_interval: Optional[int] = field(
+        default=50,
+        metadata={
+            "help": "Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update."
+        },
+    )
+    badam_update_ratio: float = field(
+        default=0.05,
+        metadata={"help": "The ratio of the update for ratio-wise BAdam."},
+    )
+    badam_mask_mode: Literal["adjacent", "scatter"] = field(
+        default="adjacent",
+        metadata={
+            "help": (
+                "The mode of the mask for BAdam optimizer. "
+                "`adjacent` means that the trainable parameters are adjacent to each other, "
+                "`scatter` means that trainable parameters are randomly choosed from the weight."
+            )
+        },
+    )
+    badam_verbose: int = field(
+        default=0,
+        metadata={
+            "help": (
+                "The verbosity level of BAdam optimizer. "
+                "0 for no print, 1 for print the block prefix, 2 for print trainable parameters."
+            )
+        },
+    )
+
+
+@dataclass
+class SwanLabArguments:
+    use_swanlab: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the SwanLab (an experiment tracking and visualization tool)."},
+    )
+    swanlab_project: Optional[str] = field(
+        default="llamafactory",
+        metadata={"help": "The project name in SwanLab."},
+    )
+    swanlab_workspace: Optional[str] = field(
+        default=None,
+        metadata={"help": "The workspace name in SwanLab."},
+    )
+    swanlab_run_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The experiment name in SwanLab."},
+    )
+    swanlab_mode: Literal["cloud", "local"] = field(
+        default="cloud",
+        metadata={"help": "The mode of SwanLab."},
+    )
+    swanlab_api_key: Optional[str] = field(
+        default=None,
+        metadata={"help": "The API key for SwanLab."},
+    )
+    swanlab_logdir: Optional[str] = field(
+        default=None,
+        metadata={"help": "The log directory for SwanLab."},
+    )
+    swanlab_lark_webhook_url: Optional[str] = field(
+        default=None,
+        metadata={"help": "The Lark(飞书) webhook URL for SwanLab."},
+    )
+    swanlab_lark_secret: Optional[str] = field(
+        default=None,
+        metadata={"help": "The Lark(飞书) secret for SwanLab."},
+    )
+
+
+@dataclass
+class FinetuningArguments(
+    SwanLabArguments, BAdamArgument, ApolloArguments, GaloreArguments, RLHFArguments, LoraArguments, FreezeArguments
+):
+    r"""Arguments pertaining to which techniques we are going to fine-tuning with."""
+
+    pure_bf16: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to train model in purely bf16 precision (without AMP)."},
+    )
+    stage: Literal["pt", "sft", "rm", "grpo", "dpo", "kto"] = field(
+        default="sft",
+        metadata={"help": "Which stage will be performed in training."},
+    )
+    finetuning_type: Literal["lora", "freeze", "full"] = field(
+        default="lora",
+        metadata={"help": "Which fine-tuning method to use."},
+    )
+    use_llama_pro: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to make only the parameters in the expanded blocks trainable."},
+    )
+    use_adam_mini: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the Adam-mini optimizer."},
+    )
+    use_muon: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the Muon optimizer."},
+    )
+    freeze_vision_tower: bool = field(
+        default=True,
+        metadata={"help": "Whether ot not to freeze the vision tower in MLLM training."},
+    )
+    freeze_multi_modal_projector: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to freeze the multi modal projector in MLLM training."},
+    )
+    freeze_language_model: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to freeze the language model in MLLM training."},
+    )
+    compute_accuracy: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to compute the token-level accuracy at evaluation."},
+    )
+    disable_shuffling: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to disable the shuffling of the training set."},
+    )
+    early_stopping_steps: Optional[int] = field(
+        default=None,
+        metadata={"help": "Number of steps to stop training if the `metric_for_best_model` does not improve."},
+    )
+    plot_loss: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to save the training loss curves."},
+    )
+    include_effective_tokens_per_second: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to compute effective tokens per second."},
+    )
+    vllm_server_base_url: str = field(
+        default='http://localhost:8000',
+        metadata={"help": "The base URL of the vLLM server."},
+    )
+
+    def __post_init__(self):
+        def split_arg(arg):
+            if isinstance(arg, str):
+                return [item.strip() for item in arg.split(",")]
+            return arg
+
+        self.freeze_trainable_modules: list[str] = split_arg(self.freeze_trainable_modules)
+        self.freeze_extra_modules: Optional[list[str]] = split_arg(self.freeze_extra_modules)
+        self.lora_alpha: int = self.lora_alpha or self.lora_rank * 2
+        self.lora_target: list[str] = split_arg(self.lora_target)
+        self.additional_target: Optional[list[str]] = split_arg(self.additional_target)
+        self.galore_target: list[str] = split_arg(self.galore_target)
+        self.apollo_target: list[str] = split_arg(self.apollo_target)
+        self.use_ref_model = self.stage == "dpo" and self.pref_loss not in ["orpo", "simpo"]
+
+        assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
+        assert self.ref_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
+        assert self.reward_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
+
+        if self.stage == "ppo" and self.reward_model is None:
+            raise ValueError("`reward_model` is necessary for PPO training.")
+
+        if self.stage == "ppo" and self.reward_model_type == "lora" and self.finetuning_type != "lora":
+            raise ValueError("`reward_model_type` cannot be lora for Freeze/Full PPO training.")
+
+        if self.stage == "dpo" and self.pref_loss != "sigmoid" and self.dpo_label_smoothing > 1e-6:
+            raise ValueError("`dpo_label_smoothing` is only valid for sigmoid loss function.")
+
+        if self.use_llama_pro and self.finetuning_type == "full":
+            raise ValueError("`use_llama_pro` is only valid for Freeze or LoRA training.")
+
+        if self.finetuning_type == "lora" and (self.use_galore or self.use_apollo or self.use_badam):
+            raise ValueError("Cannot use LoRA with GaLore, APOLLO or BAdam together.")
+
+        if int(self.use_galore) + int(self.use_apollo) + (self.use_badam) > 1:
+            raise ValueError("Cannot use GaLore, APOLLO or BAdam together.")
+
+        if self.pissa_init and (self.stage in ["ppo", "kto"] or self.use_ref_model):
+            raise ValueError("Cannot use PiSSA for current training stage.")
+
+        if self.finetuning_type != "lora":
+            if self.loraplus_lr_ratio is not None:
+                raise ValueError("`loraplus_lr_ratio` is only valid for LoRA training.")
+
+            if self.use_rslora:
+                raise ValueError("`use_rslora` is only valid for LoRA training.")
+
+            if self.use_dora:
+                raise ValueError("`use_dora` is only valid for LoRA training.")
+
+            if self.pissa_init:
+                raise ValueError("`pissa_init` is only valid for LoRA training.")
+
+    def to_dict(self) -> dict[str, Any]:
+        args = asdict(self)
+        args = {k: f"<{k.upper()}>" if k.endswith("api_key") else v for k, v in args.items()}
+        return args

src/llamafactory/hparams/generating_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.
+
+from dataclasses import asdict, dataclass, field
+from typing import Any
+
+from transformers import GenerationConfig
+
+
+@dataclass
+class GeneratingArguments:
+    r"""Arguments pertaining to specify the decoding parameters."""
+
+    do_sample: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."},
+    )
+    temperature: float = field(
+        default=0.95,
+        metadata={"help": "The value used to modulate the next token probabilities."},
+    )
+    top_p: float = field(
+        default=0.7,
+        metadata={
+            "help": (
+                "The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept."
+            )
+        },
+    )
+    top_k: int = field(
+        default=50,
+        metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."},
+    )
+    num_beams: int = field(
+        default=1,
+        metadata={"help": "Number of beams for beam search. 1 means no beam search."},
+    )
+    max_length: int = field(
+        default=1024,
+        metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."},
+    )
+    max_new_tokens: int = field(
+        default=1024,
+        metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
+    )
+    repetition_penalty: float = field(
+        default=1.0,
+        metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."},
+    )
+    length_penalty: float = field(
+        default=1.0,
+        metadata={"help": "Exponential penalty to the length that is used with beam-based generation."},
+    )
+    skip_special_tokens: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to remove special tokens in the decoding."},
+    )
+
+    def to_dict(self, obey_generation_config: bool = False) -> dict[str, Any]:
+        args = asdict(self)
+        if args.get("max_new_tokens", -1) > 0:
+            args.pop("max_length", None)
+        else:
+            args.pop("max_new_tokens", None)
+
+        if obey_generation_config:
+            generation_config = GenerationConfig()
+            for key in list(args.keys()):
+                if not hasattr(generation_config, key):
+                    args.pop(key)
+
+        return args

src/llamafactory/hparams/model_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.
+
+import json
+from dataclasses import asdict, dataclass, field, fields
+from typing import Any, Literal, Optional, Union
+
+import torch
+from transformers.training_args import _convert_str_dict
+from typing_extensions import Self
+
+from ..extras.constants import AttentionFunction, EngineName, QuantizationMethod, RopeScaling
+
+
+@dataclass
+class BaseModelArguments:
+    r"""Arguments pertaining to the model."""
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models."
+        },
+    )
+    adapter_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Path to the adapter weight or identifier from huggingface.co/models. "
+                "Use commas to separate multiple adapters."
+            )
+        },
+    )
+    adapter_folder: Optional[str] = field(
+        default=None,
+        metadata={"help": "The folder containing the adapter weights to load."},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use one of the fast tokenizer (backed by the tokenizers library)."},
+    )
+    resize_vocab: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to resize the tokenizer vocab and the embedding layers."},
+    )
+    split_special_tokens: bool = field(
+        default=False,
+        metadata={"help": "Whether or not the special tokens should be split during the tokenization process."},
+    )
+    add_tokens: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Non-special tokens to be added into the tokenizer. Use commas to separate multiple tokens."
+        },
+    )
+    add_special_tokens: Optional[str] = field(
+        default=None,
+        metadata={"help": "Special tokens to be added into the tokenizer. Use commas to separate multiple tokens."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    low_cpu_mem_usage: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use memory-efficient model loading."},
+    )
+    rope_scaling: Optional[RopeScaling] = field(
+        default=None,
+        metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
+    )
+    flash_attn: AttentionFunction = field(
+        default=AttentionFunction.AUTO,
+        metadata={"help": "Enable FlashAttention for faster training and inference."},
+    )
+    shift_attn: bool = field(
+        default=False,
+        metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."},
+    )
+    mixture_of_depths: Optional[Literal["convert", "load"]] = field(
+        default=None,
+        metadata={"help": "Convert the model to mixture-of-depths (MoD) or load the MoD model."},
+    )
+    use_unsloth: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."},
+    )
+    use_unsloth_gc: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use unsloth's gradient checkpointing (no need to install unsloth)."},
+    )
+    enable_liger_kernel: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to enable liger kernel for faster training."},
+    )
+    moe_aux_loss_coef: Optional[float] = field(
+        default=None,
+        metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},
+    )
+    disable_gradient_checkpointing: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to disable gradient checkpointing."},
+    )
+    use_reentrant_gc: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use reentrant gradient checkpointing."},
+    )
+    upcast_layernorm: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to upcast the layernorm weights in fp32."},
+    )
+    upcast_lmhead_output: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to upcast the output of lm_head in fp32."},
+    )
+    train_from_scratch: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to randomly initialize the model weights."},
+    )
+    infer_backend: EngineName = field(
+        default=EngineName.HF,
+        metadata={"help": "Backend engine used at inference."},
+    )
+    offload_folder: str = field(
+        default="offload",
+        metadata={"help": "Path to offload model weights."},
+    )
+    use_cache: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use KV cache in generation."},
+    )
+    infer_dtype: Literal["auto", "float16", "bfloat16", "float32"] = field(
+        default="auto",
+        metadata={"help": "Data type for model weights and activations at inference."},
+    )
+    hf_hub_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with Hugging Face Hub."},
+    )
+    ms_hub_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with ModelScope Hub."},
+    )
+    om_hub_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with Modelers Hub."},
+    )
+    print_param_status: bool = field(
+        default=False,
+        metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={"help": "Whether to trust the execution of code from datasets/models defined on the Hub or not."},
+    )
+
+    def __post_init__(self):
+        if self.model_name_or_path is None:
+            raise ValueError("Please provide `model_name_or_path`.")
+
+        if self.split_special_tokens and self.use_fast_tokenizer:
+            raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
+
+        if self.adapter_name_or_path is not None:  # support merging multiple lora weights
+            self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")]
+
+        if self.add_tokens is not None:  # support multiple tokens
+            self.add_tokens = [token.strip() for token in self.add_tokens.split(",")]
+
+        if self.add_special_tokens is not None:  # support multiple special tokens
+            self.add_special_tokens = [token.strip() for token in self.add_special_tokens.split(",")]
+
+
+@dataclass
+class QuantizationArguments:
+    r"""Arguments pertaining to the quantization method."""
+
+    quantization_method: QuantizationMethod = field(
+        default=QuantizationMethod.BNB,
+        metadata={"help": "Quantization method to use for on-the-fly quantization."},
+    )
+    quantization_bit: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of bits to quantize the model using on-the-fly quantization."},
+    )
+    quantization_type: Literal["fp4", "nf4"] = field(
+        default="nf4",
+        metadata={"help": "Quantization data type to use in bitsandbytes int4 training."},
+    )
+    double_quantization: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use double quantization in bitsandbytes int4 training."},
+    )
+    quantization_device_map: Optional[Literal["auto"]] = field(
+        default=None,
+        metadata={"help": "Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0."},
+    )
+
+
+@dataclass
+class ProcessorArguments:
+    r"""Arguments pertaining to the image processor."""
+
+    image_max_pixels: int = field(
+        default=768 * 768,
+        metadata={"help": "The maximum number of pixels of image inputs."},
+    )
+    image_min_pixels: int = field(
+        default=32 * 32,
+        metadata={"help": "The minimum number of pixels of image inputs."},
+    )
+    image_do_pan_and_scan: bool = field(
+        default=False,
+        metadata={"help": "Use pan and scan to process image for gemma3."},
+    )
+    crop_to_patches: bool = field(
+        default=False,
+        metadata={"help": "Whether to crop the image to patches for internvl."},
+    )
+    video_max_pixels: int = field(
+        default=256 * 256,
+        metadata={"help": "The maximum number of pixels of video inputs."},
+    )
+    video_min_pixels: int = field(
+        default=16 * 16,
+        metadata={"help": "The minimum number of pixels of video inputs."},
+    )
+    video_fps: float = field(
+        default=2.0,
+        metadata={"help": "The frames to sample per second for video inputs."},
+    )
+    video_maxlen: int = field(
+        default=128,
+        metadata={"help": "The maximum number of sampled frames for video inputs."},
+    )
+    use_audio_in_video: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use audio in video inputs."},
+    )
+    audio_sampling_rate: int = field(
+        default=16000,
+        metadata={"help": "The sampling rate of audio inputs."},
+    )
+
+    def __post_init__(self):
+        if self.image_max_pixels < self.image_min_pixels:
+            raise ValueError("`image_max_pixels` cannot be smaller than `image_min_pixels`.")
+
+        if self.video_max_pixels < self.video_min_pixels:
+            raise ValueError("`video_max_pixels` cannot be smaller than `video_min_pixels`.")
+
+
+@dataclass
+class ExportArguments:
+    r"""Arguments pertaining to the model export."""
+
+    export_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory to save the exported model."},
+    )
+    export_size: int = field(
+        default=5,
+        metadata={"help": "The file shard size (in GB) of the exported model."},
+    )
+    export_device: Literal["cpu", "auto"] = field(
+        default="cpu",
+        metadata={"help": "The device used in model export, use `auto` to accelerate exporting."},
+    )
+    export_quantization_bit: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of bits to quantize the exported model."},
+    )
+    export_quantization_dataset: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the dataset or dataset name to use in quantizing the exported model."},
+    )
+    export_quantization_nsamples: int = field(
+        default=128,
+        metadata={"help": "The number of samples used for quantization."},
+    )
+    export_quantization_maxlen: int = field(
+        default=1024,
+        metadata={"help": "The maximum length of the model inputs used for quantization."},
+    )
+    export_legacy_format: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to save the `.bin` files instead of `.safetensors`."},
+    )
+    export_hub_model_id: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the repository if push the model to the Hugging Face hub."},
+    )
+
+    def __post_init__(self):
+        if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
+            raise ValueError("Quantization dataset is necessary for exporting.")
+
+
+@dataclass
+class VllmArguments:
+    r"""Arguments pertaining to the vLLM worker."""
+
+    vllm_maxlen: int = field(
+        default=4096,
+        metadata={"help": "Maximum sequence (prompt + response) length of the vLLM engine."},
+    )
+    vllm_gpu_util: float = field(
+        default=0.7,
+        metadata={"help": "The fraction of GPU memory in (0,1) to be used for the vLLM engine."},
+    )
+    vllm_enforce_eager: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to disable CUDA graph in the vLLM engine."},
+    )
+    vllm_max_lora_rank: int = field(
+        default=32,
+        metadata={"help": "Maximum rank of all LoRAs in the vLLM engine."},
+    )
+    vllm_config: Optional[Union[dict, str]] = field(
+        default=None,
+        metadata={"help": "Config to initialize the vllm engine. Please use JSON strings."},
+    )
+
+    def __post_init__(self):
+        if isinstance(self.vllm_config, str) and self.vllm_config.startswith("{"):
+            self.vllm_config = _convert_str_dict(json.loads(self.vllm_config))
+
+
+@dataclass
+class SGLangArguments:
+    r"""Arguments pertaining to the SGLang worker."""
+
+    sglang_maxlen: int = field(
+        default=4096,
+        metadata={"help": "Maximum sequence (prompt + response) length of the SGLang engine."},
+    )
+    sglang_mem_fraction: float = field(
+        default=0.7,
+        metadata={"help": "The memory fraction (0-1) to be used for the SGLang engine."},
+    )
+    sglang_tp_size: int = field(
+        default=-1,
+        metadata={"help": "Tensor parallel size for the SGLang engine."},
+    )
+    sglang_config: Optional[Union[dict, str]] = field(
+        default=None,
+        metadata={"help": "Config to initialize the SGLang engine. Please use JSON strings."},
+    )
+    sglang_lora_backend: Literal["triton", "flashinfer"] = field(
+        default="triton",
+        metadata={
+            "help": "The backend of running GEMM kernels for Lora modules. Recommend using the Triton LoRA backend for better performance and stability."
+        },
+    )
+
+    def __post_init__(self):
+        if isinstance(self.sglang_config, str) and self.sglang_config.startswith("{"):
+            self.sglang_config = _convert_str_dict(json.loads(self.sglang_config))
+
+
+@dataclass
+class ModelArguments(
+    SGLangArguments, VllmArguments, ExportArguments, ProcessorArguments, QuantizationArguments, BaseModelArguments
+):
+    r"""Arguments pertaining to which model/config/tokenizer we are going to fine-tune or infer.
+
+    The class on the most right will be displayed first.
+    """
+
+    compute_dtype: Optional[torch.dtype] = field(
+        default=None,
+        init=False,
+        metadata={"help": "Torch data type for computing model outputs, derived from `fp/bf16`. Do not specify it."},
+    )
+    device_map: Optional[Union[str, dict[str, Any]]] = field(
+        default=None,
+        init=False,
+        metadata={"help": "Device map for model placement, derived from training stage. Do not specify it."},
+    )
+    model_max_length: Optional[int] = field(
+        default=None,
+        init=False,
+        metadata={"help": "The maximum input length for model, derived from `cutoff_len`. Do not specify it."},
+    )
+    block_diag_attn: bool = field(
+        default=False,
+        init=False,
+        metadata={"help": "Whether use block diag attention or not, derived from `neat_packing`. Do not specify it."},
+    )
+
+    def __post_init__(self):
+        BaseModelArguments.__post_init__(self)
+        ProcessorArguments.__post_init__(self)
+        ExportArguments.__post_init__(self)
+        VllmArguments.__post_init__(self)
+        SGLangArguments.__post_init__(self)
+
+    @classmethod
+    def copyfrom(cls, source: "Self", **kwargs) -> "Self":
+        init_args, lazy_args = {}, {}
+        for attr in fields(source):
+            if attr.init:
+                init_args[attr.name] = getattr(source, attr.name)
+            else:
+                lazy_args[attr.name] = getattr(source, attr.name)
+
+        init_args.update(kwargs)
+        result = cls(**init_args)
+        for name, value in lazy_args.items():
+            setattr(result, name, value)
+
+        return result
+
+    def to_dict(self) -> dict[str, Any]:
+        args = asdict(self)
+        args = {k: f"<{k.upper()}>" if k.endswith("token") else v for k, v in args.items()}
+        return args

src/llamafactory/hparams/parser.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.
+
+import os
+import sys
+from pathlib import Path
+from typing import Any, Optional, Union
+
+import torch
+import transformers
+from omegaconf import OmegaConf
+from transformers import HfArgumentParser
+from transformers.integrations import is_deepspeed_zero3_enabled
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.training_args import ParallelMode
+from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available
+
+from ..extras import logging
+from ..extras.constants import CHECKPOINT_NAMES, EngineName
+from ..extras.misc import check_dependencies, check_version, get_current_device, is_env_enabled
+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 .training_args import RayArguments, TrainingArguments
+
+
+logger = logging.get_logger(__name__)
+
+check_dependencies()
+
+
+_TRAIN_ARGS = [ModelArguments, DataArguments, TrainingArguments, FinetuningArguments, GeneratingArguments]
+_TRAIN_CLS = tuple[ModelArguments, DataArguments, TrainingArguments, FinetuningArguments, GeneratingArguments]
+_INFER_ARGS = [ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]
+_INFER_CLS = tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]
+_EVAL_ARGS = [ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
+_EVAL_CLS = tuple[ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
+
+
+def read_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> Union[dict[str, Any], list[str]]:
+    r"""Get arguments from the command line or a config file."""
+    if args is not None:
+        return args
+
+    if sys.argv[1].endswith(".yaml") or sys.argv[1].endswith(".yml"):
+        override_config = OmegaConf.from_cli(sys.argv[2:])
+        dict_config = OmegaConf.load(Path(sys.argv[1]).absolute())
+        return OmegaConf.to_container(OmegaConf.merge(dict_config, override_config))
+    elif sys.argv[1].endswith(".json"):
+        override_config = OmegaConf.from_cli(sys.argv[2:])
+        dict_config = OmegaConf.load(Path(sys.argv[1]).absolute())
+        return OmegaConf.to_container(OmegaConf.merge(dict_config, override_config))
+    else:
+        return sys.argv[1:]
+
+
+def _parse_args(
+    parser: "HfArgumentParser", args: Optional[Union[dict[str, Any], list[str]]] = None, allow_extra_keys: bool = False
+) -> tuple[Any]:
+    args = read_args(args)
+    if isinstance(args, dict):
+        return parser.parse_dict(args, allow_extra_keys=allow_extra_keys)
+
+    (*parsed_args, unknown_args) = parser.parse_args_into_dataclasses(args=args, return_remaining_strings=True)
+
+    if unknown_args and not allow_extra_keys:
+        print(parser.format_help())
+        print(f"Got unknown args, potentially deprecated arguments: {unknown_args}")
+        raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {unknown_args}")
+
+    return tuple(parsed_args)
+
+
+def _set_transformers_logging() -> None:
+    if os.getenv("LLAMAFACTORY_VERBOSITY", "INFO") in ["DEBUG", "INFO"]:
+        transformers.utils.logging.set_verbosity_info()
+        transformers.utils.logging.enable_default_handler()
+        transformers.utils.logging.enable_explicit_format()
+
+
+def _set_env_vars() -> None:
+    if is_torch_npu_available():
+        # avoid JIT compile on NPU devices, see https://zhuanlan.zhihu.com/p/660875458
+        torch.npu.set_compile_mode(jit_compile=is_env_enabled("NPU_JIT_COMPILE"))
+        # avoid use fork method on NPU devices, see https://github.com/hiyouga/LLaMA-Factory/issues/7447
+        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+
+def _verify_model_args(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    finetuning_args: "FinetuningArguments",
+) -> None:
+    if model_args.adapter_name_or_path is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Adapter is only valid for the LoRA method.")
+
+    if model_args.quantization_bit is not None:
+        if finetuning_args.finetuning_type != "lora":
+            raise ValueError("Quantization is only compatible with the LoRA method.")
+
+        if finetuning_args.pissa_init:
+            raise ValueError("Please use scripts/pissa_init.py to initialize PiSSA for a quantized model.")
+
+        if model_args.resize_vocab:
+            raise ValueError("Cannot resize embedding layers of a quantized model.")
+
+        if model_args.adapter_name_or_path is not None and finetuning_args.create_new_adapter:
+            raise ValueError("Cannot create new adapter upon a quantized model.")
+
+        if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
+            raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
+
+    if data_args.template == "yi" and model_args.use_fast_tokenizer:
+        logger.warning_rank0("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
+        model_args.use_fast_tokenizer = False
+
+
+def _check_extra_dependencies(
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    training_args: Optional["TrainingArguments"] = None,
+) -> None:
+    if model_args.use_unsloth:
+        check_version("unsloth", mandatory=True)
+
+    if model_args.enable_liger_kernel:
+        check_version("liger-kernel", mandatory=True)
+
+    if model_args.mixture_of_depths is not None:
+        check_version("mixture-of-depth>=1.1.6", mandatory=True)
+
+    if model_args.infer_backend == EngineName.VLLM:
+        check_version("vllm>=0.4.3,<=0.9.1")
+        check_version("vllm", mandatory=True)
+    elif model_args.infer_backend == EngineName.SGLANG:
+        check_version("sglang>=0.4.5")
+        check_version("sglang", mandatory=True)
+
+    if finetuning_args.use_galore:
+        check_version("galore_torch", mandatory=True)
+
+    if finetuning_args.use_apollo:
+        check_version("apollo_torch", mandatory=True)
+
+    if finetuning_args.use_badam:
+        check_version("badam>=1.2.1", mandatory=True)
+
+    if finetuning_args.use_adam_mini:
+        check_version("adam-mini", mandatory=True)
+
+    if finetuning_args.use_swanlab:
+        check_version("swanlab", mandatory=True)
+
+    if finetuning_args.plot_loss:
+        check_version("matplotlib", mandatory=True)
+
+    if training_args is not None:
+        if training_args.deepspeed:
+            # pin deepspeed version < 0.17 because of https://github.com/deepspeedai/DeepSpeed/issues/7347
+            check_version("deepspeed>=0.10.0,<=0.16.9", mandatory=True)
+
+        if training_args.predict_with_generate:
+            check_version("jieba", mandatory=True)
+            check_version("nltk", mandatory=True)
+            check_version("rouge_chinese", mandatory=True)
+
+
+def _parse_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _TRAIN_CLS:
+    parser = HfArgumentParser(_TRAIN_ARGS)
+    allow_extra_keys = is_env_enabled("ALLOW_EXTRA_ARGS")
+    return _parse_args(parser, args, allow_extra_keys=allow_extra_keys)
+
+
+def _parse_infer_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _INFER_CLS:
+    parser = HfArgumentParser(_INFER_ARGS)
+    allow_extra_keys = is_env_enabled("ALLOW_EXTRA_ARGS")
+    return _parse_args(parser, args, allow_extra_keys=allow_extra_keys)
+
+
+def _parse_eval_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _EVAL_CLS:
+    parser = HfArgumentParser(_EVAL_ARGS)
+    allow_extra_keys = is_env_enabled("ALLOW_EXTRA_ARGS")
+    return _parse_args(parser, args, allow_extra_keys=allow_extra_keys)
+
+
+def get_ray_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> RayArguments:
+    parser = HfArgumentParser(RayArguments)
+    (ray_args,) = _parse_args(parser, args, allow_extra_keys=True)
+    return ray_args
+
+
+def get_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _TRAIN_CLS:
+    model_args, data_args, training_args, finetuning_args, generating_args = _parse_train_args(args)
+
+    # Setup logging
+    if training_args.should_log:
+        _set_transformers_logging()
+
+    # Check arguments
+    if finetuning_args.stage != "sft":
+        if training_args.predict_with_generate:
+            raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
+
+        if data_args.neat_packing:
+            raise ValueError("`neat_packing` cannot be set as True except SFT.")
+
+        if data_args.train_on_prompt or data_args.mask_history:
+            raise ValueError("`train_on_prompt` or `mask_history` cannot be set as True except SFT.")
+
+    if finetuning_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
+        raise ValueError("Please enable `predict_with_generate` to save model predictions.")
+
+    if finetuning_args.stage in ["rm", "ppo"] and training_args.load_best_model_at_end:
+        raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
+
+    if finetuning_args.stage == "ppo":
+        if not training_args.do_train:
+            raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
+
+        if model_args.shift_attn:
+            raise ValueError("PPO training is incompatible with S^2-Attn.")
+
+        if finetuning_args.reward_model_type == "lora" and model_args.use_unsloth:
+            raise ValueError("Unsloth does not support lora reward model.")
+
+        if training_args.report_to and training_args.report_to[0] not in ["wandb", "tensorboard"]:
+            raise ValueError("PPO only accepts wandb or tensorboard logger.")
+
+    if training_args.parallel_mode == ParallelMode.NOT_DISTRIBUTED:
+        raise ValueError("Please launch distributed training with `llamafactory-cli` or `torchrun`.")
+
+    if training_args.deepspeed and training_args.parallel_mode != ParallelMode.DISTRIBUTED:
+        raise ValueError("Please use `FORCE_TORCHRUN=1` to launch DeepSpeed training.")
+
+    if training_args.max_steps == -1 and data_args.streaming:
+        raise ValueError("Please specify `max_steps` in streaming mode.")
+
+    if training_args.do_train and data_args.dataset is None:
+        raise ValueError("Please specify dataset for training.")
+
+    if (training_args.do_eval or training_args.do_predict) and (
+        data_args.eval_dataset is None and data_args.val_size < 1e-6
+    ):
+        raise ValueError("Please specify dataset for evaluation.")
+
+    if training_args.predict_with_generate:
+        if is_deepspeed_zero3_enabled():
+            raise ValueError("`predict_with_generate` is incompatible with DeepSpeed ZeRO-3.")
+
+        if data_args.eval_dataset is None:
+            raise ValueError("Cannot use `predict_with_generate` if `eval_dataset` is None.")
+
+        if finetuning_args.compute_accuracy:
+            raise ValueError("Cannot use `predict_with_generate` and `compute_accuracy` together.")
+
+    if training_args.do_train and model_args.quantization_device_map == "auto":
+        raise ValueError("Cannot use device map for quantized models in training.")
+
+    if finetuning_args.pissa_init and is_deepspeed_zero3_enabled():
+        raise ValueError("Please use scripts/pissa_init.py to initialize PiSSA in DeepSpeed ZeRO-3.")
+
+    if finetuning_args.pure_bf16:
+        if not (is_torch_bf16_gpu_available() or (is_torch_npu_available() and torch.npu.is_bf16_supported())):
+            raise ValueError("This device does not support `pure_bf16`.")
+
+        if is_deepspeed_zero3_enabled():
+            raise ValueError("`pure_bf16` is incompatible with DeepSpeed ZeRO-3.")
+
+    if training_args.parallel_mode == ParallelMode.DISTRIBUTED:
+        if finetuning_args.use_galore and finetuning_args.galore_layerwise:
+            raise ValueError("Distributed training does not support layer-wise GaLore.")
+
+        if finetuning_args.use_apollo and finetuning_args.apollo_layerwise:
+            raise ValueError("Distributed training does not support layer-wise APOLLO.")
+
+        if finetuning_args.use_badam:
+            if finetuning_args.badam_mode == "ratio":
+                raise ValueError("Radio-based BAdam does not yet support distributed training, use layer-wise BAdam.")
+            elif not is_deepspeed_zero3_enabled():
+                raise ValueError("Layer-wise BAdam only supports DeepSpeed ZeRO-3 training.")
+
+    if training_args.deepspeed is not None and (finetuning_args.use_galore or finetuning_args.use_apollo):
+        raise ValueError("GaLore and APOLLO are incompatible with DeepSpeed yet.")
+
+    if model_args.infer_backend != EngineName.HF:
+        raise ValueError("vLLM/SGLang backend is only available for API, CLI and Web.")
+
+    if model_args.use_unsloth and is_deepspeed_zero3_enabled():
+        raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")
+
+    _set_env_vars()
+    _verify_model_args(model_args, data_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args, training_args)
+
+    if (
+        training_args.do_train
+        and finetuning_args.finetuning_type == "lora"
+        and model_args.quantization_bit is None
+        and model_args.resize_vocab
+        and finetuning_args.additional_target is None
+    ):
+        logger.warning_rank0(
+            "Remember to add embedding layers to `additional_target` to make the added tokens trainable."
+        )
+
+    if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
+        logger.warning_rank0("We recommend enable `upcast_layernorm` in quantized training.")
+
+    if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
+        logger.warning_rank0("We recommend enable mixed precision training.")
+
+    if (
+        training_args.do_train
+        and (finetuning_args.use_galore or finetuning_args.use_apollo)
+        and not finetuning_args.pure_bf16
+    ):
+        logger.warning_rank0(
+            "Using GaLore or APOLLO with mixed precision training may significantly increases GPU memory usage."
+        )
+
+    if (not training_args.do_train) and model_args.quantization_bit is not None:
+        logger.warning_rank0("Evaluating model in 4/8-bit mode may cause lower scores.")
+
+    if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
+        logger.warning_rank0("Specify `ref_model` for computing rewards at evaluation.")
+
+    # Post-process training arguments
+    training_args.generation_max_length = training_args.generation_max_length or data_args.cutoff_len
+    training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
+    training_args.remove_unused_columns = False  # important for multimodal dataset
+
+    if finetuning_args.finetuning_type == "lora":
+        # https://github.com/huggingface/transformers/blob/v4.50.0/src/transformers/trainer.py#L782
+        training_args.label_names = training_args.label_names or ["labels"]
+
+    if "swanlab" in training_args.report_to and finetuning_args.use_swanlab:
+        training_args.report_to.remove("swanlab")
+
+    if (
+        training_args.parallel_mode == ParallelMode.DISTRIBUTED
+        and training_args.ddp_find_unused_parameters is None
+        and finetuning_args.finetuning_type == "lora"
+    ):
+        logger.info_rank0("Set `ddp_find_unused_parameters` to False in DDP training since LoRA is enabled.")
+        training_args.ddp_find_unused_parameters = False
+
+    if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type in ["full", "freeze"]:
+        can_resume_from_checkpoint = False
+        if training_args.resume_from_checkpoint is not None:
+            logger.warning_rank0("Cannot resume from checkpoint in current stage.")
+            training_args.resume_from_checkpoint = None
+    else:
+        can_resume_from_checkpoint = True
+
+    if (
+        training_args.resume_from_checkpoint is None
+        and training_args.do_train
+        and os.path.isdir(training_args.output_dir)
+        and not training_args.overwrite_output_dir
+        and can_resume_from_checkpoint
+    ):
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and any(
+            os.path.isfile(os.path.join(training_args.output_dir, name)) for name in CHECKPOINT_NAMES
+        ):
+            raise ValueError("Output directory already exists and is not empty. Please set `overwrite_output_dir`.")
+
+        if last_checkpoint is not None:
+            training_args.resume_from_checkpoint = last_checkpoint
+            logger.info_rank0(f"Resuming training from {training_args.resume_from_checkpoint}.")
+            logger.info_rank0("Change `output_dir` or use `overwrite_output_dir` to avoid.")
+
+    if (
+        finetuning_args.stage in ["rm", "ppo"]
+        and finetuning_args.finetuning_type == "lora"
+        and training_args.resume_from_checkpoint is not None
+    ):
+        logger.warning_rank0(
+            f"Add {training_args.resume_from_checkpoint} to `adapter_name_or_path` to resume training from checkpoint."
+        )
+
+    # Post-process model arguments
+    if training_args.bf16 or finetuning_args.pure_bf16:
+        model_args.compute_dtype = torch.bfloat16
+    elif training_args.fp16:
+        model_args.compute_dtype = torch.float16
+
+    model_args.device_map = {"": get_current_device()}
+    model_args.model_max_length = data_args.cutoff_len
+    model_args.block_diag_attn = data_args.neat_packing
+    data_args.packing = data_args.packing if data_args.packing is not None else finetuning_args.stage == "pt"
+
+    # Log on each process the small summary
+    logger.info(
+        f"Process rank: {training_args.process_index}, "
+        f"world size: {training_args.world_size}, device: {training_args.device}, "
+        f"distributed training: {training_args.parallel_mode == ParallelMode.DISTRIBUTED}, "
+        f"compute dtype: {str(model_args.compute_dtype)}"
+    )
+    transformers.set_seed(training_args.seed)
+
+    return model_args, data_args, training_args, finetuning_args, generating_args
+
+
+def get_infer_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _INFER_CLS:
+    model_args, data_args, finetuning_args, generating_args = _parse_infer_args(args)
+
+    # Setup logging
+    _set_transformers_logging()
+
+    # Check arguments
+    if model_args.infer_backend == "vllm":
+        if finetuning_args.stage != "sft":
+            raise ValueError("vLLM engine only supports auto-regressive models.")
+
+        if model_args.quantization_bit is not None:
+            raise ValueError("vLLM engine does not support bnb quantization (GPTQ and AWQ are supported).")
+
+        if model_args.rope_scaling is not None:
+            raise ValueError("vLLM engine does not support RoPE scaling.")
+
+        if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
+            raise ValueError("vLLM only accepts a single adapter. Merge them first.")
+
+    _set_env_vars()
+    _verify_model_args(model_args, data_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args)
+
+    # Post-process model arguments
+    if model_args.export_dir is not None and model_args.export_device == "cpu":
+        model_args.device_map = {"": torch.device("cpu")}
+        if data_args.cutoff_len != DataArguments().cutoff_len:  # override cutoff_len if it is not default
+            model_args.model_max_length = data_args.cutoff_len
+    else:
+        model_args.device_map = "auto"
+
+    return model_args, data_args, finetuning_args, generating_args
+
+
+def get_eval_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _EVAL_CLS:
+    model_args, data_args, eval_args, finetuning_args = _parse_eval_args(args)
+
+    # Setup logging
+    _set_transformers_logging()
+
+    # Check arguments
+    if model_args.infer_backend != EngineName.HF:
+        raise ValueError("vLLM/SGLang backend is only available for API, CLI and Web.")
+
+    _set_env_vars()
+    _verify_model_args(model_args, data_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args)
+
+    model_args.device_map = "auto"
+
+    transformers.set_seed(eval_args.seed)
+
+    return model_args, data_args, eval_args, finetuning_args

src/llamafactory/hparams/training_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 json
+from dataclasses import dataclass, field
+from typing import Literal, Optional, Union
+
+from transformers import Seq2SeqTrainingArguments
+from transformers.training_args import _convert_str_dict
+
+from ..extras.misc import use_ray
+
+
+@dataclass
+class RayArguments:
+    r"""Arguments pertaining to the Ray training."""
+
+    ray_run_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The training results will be saved at `<ray_storage_path>/ray_run_name`."},
+    )
+    ray_storage_path: str = field(
+        default="./saves",
+        metadata={"help": "The storage path to save training results to"},
+    )
+    ray_storage_filesystem: Optional[Literal["s3", "gs", "gcs"]] = field(
+        default=None,
+        metadata={"help": "The storage filesystem to use. If None specified, local filesystem will be used."},
+    )
+    ray_num_workers: int = field(
+        default=1,
+        metadata={"help": "The number of workers for Ray training. Default is 1 worker."},
+    )
+    resources_per_worker: Union[dict, str] = field(
+        default_factory=lambda: {"GPU": 1},
+        metadata={"help": "The resources per worker for Ray training. Default is to use 1 GPU per worker."},
+    )
+    placement_strategy: Literal["SPREAD", "PACK", "STRICT_SPREAD", "STRICT_PACK"] = field(
+        default="PACK",
+        metadata={"help": "The placement strategy for Ray training. Default is PACK."},
+    )
+    ray_init_kwargs: Optional[dict] = field(
+        default=None,
+        metadata={"help": "The arguments to pass to ray.init for Ray training. Default is None."},
+    )
+
+    def __post_init__(self):
+        self.use_ray = use_ray()
+        if isinstance(self.resources_per_worker, str) and self.resources_per_worker.startswith("{"):
+            self.resources_per_worker = _convert_str_dict(json.loads(self.resources_per_worker))
+        if self.ray_storage_filesystem is not None:
+            if self.ray_storage_filesystem not in ["s3", "gs", "gcs"]:
+                raise ValueError(
+                    f"ray_storage_filesystem must be one of ['s3', 'gs', 'gcs'], got {self.ray_storage_filesystem}"
+                )
+
+            import pyarrow.fs as fs
+
+            if self.ray_storage_filesystem == "s3":
+                self.ray_storage_filesystem = fs.S3FileSystem()
+            elif self.ray_storage_filesystem == "gs" or self.ray_storage_filesystem == "gcs":
+                self.ray_storage_filesystem = fs.GcsFileSystem()
+
+
+@dataclass
+class TrainingArguments(RayArguments, Seq2SeqTrainingArguments):
+    r"""Arguments pertaining to the trainer."""
+
+    def __post_init__(self):
+        Seq2SeqTrainingArguments.__post_init__(self)
+        RayArguments.__post_init__(self)

src/llamafactory/launcher.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 llamafactory.train.tuner import run_exp  # use absolute import
+
+
+def launch():
+    run_exp()
+
+
+if __name__ == "__main__":
+    launch()

src/llamafactory/model/__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 .loader import load_config, load_model, load_tokenizer
+from .model_utils.misc import find_all_linear_modules
+from .model_utils.quantization import QuantizationMethod
+from .model_utils.valuehead import load_valuehead_params
+
+
+__all__ = [
+    "QuantizationMethod",
+    "find_all_linear_modules",
+    "load_config",
+    "load_model",
+    "load_tokenizer",
+    "load_valuehead_params",
+]

src/llamafactory/model/adapter.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 typing import TYPE_CHECKING
+
+import torch
+from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
+from transformers.integrations import is_deepspeed_zero3_enabled
+
+from ..extras import logging
+from .model_utils.misc import find_all_linear_modules, find_expanded_modules
+from .model_utils.quantization import QuantizationMethod
+from .model_utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
+from .model_utils.visual import COMPOSITE_MODELS, get_forbidden_modules, patch_target_modules
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig, PreTrainedModel
+
+    from ..hparams import FinetuningArguments, ModelArguments
+
+
+logger = logging.get_logger(__name__)
+
+
+def _setup_full_tuning(
+    model: "PreTrainedModel",
+    finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
+    cast_trainable_params_to_fp32: bool,
+) -> None:
+    if not is_trainable:
+        return
+
+    logger.info_rank0("Fine-tuning method: Full")
+    forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
+    for name, param in model.named_parameters():
+        if not any(forbidden_module in name for forbidden_module in forbidden_modules):
+            if cast_trainable_params_to_fp32:
+                param.data = param.data.to(torch.float32)
+        else:
+            param.requires_grad_(False)
+
+
+def _setup_freeze_tuning(
+    model: "PreTrainedModel",
+    finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
+    cast_trainable_params_to_fp32: bool,
+) -> None:
+    if not is_trainable:
+        return
+
+    logger.info_rank0("Fine-tuning method: Freeze")
+    if hasattr(model.config, "text_config"):  # composite models
+        config = getattr(model.config, "text_config")
+    else:
+        config = model.config
+
+    num_layers = (
+        getattr(config, "num_hidden_layers", None)
+        or getattr(config, "num_layers", None)
+        or getattr(config, "n_layer", None)
+    )
+    if not num_layers:
+        raise ValueError("Current model does not support freeze tuning.")
+
+    if finetuning_args.use_llama_pro:
+        if num_layers % finetuning_args.freeze_trainable_layers != 0:
+            raise ValueError(
+                f"`num_layers` {num_layers} should be "
+                f"divisible by `num_layer_trainable` {finetuning_args.freeze_trainable_layers}."
+            )
+
+        stride = num_layers // finetuning_args.freeze_trainable_layers
+        trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
+    elif finetuning_args.freeze_trainable_layers > 0:  # fine-tuning the last n layers if num_layer_trainable > 0
+        trainable_layer_ids = range(max(0, num_layers - finetuning_args.freeze_trainable_layers), num_layers)
+    else:  # fine-tuning the first n layers if num_layer_trainable < 0
+        trainable_layer_ids = range(min(-finetuning_args.freeze_trainable_layers, num_layers))
+
+    hidden_modules = set()
+    non_hidden_modules = set()
+    for name, _ in model.named_parameters():
+        if ".0." in name:
+            hidden_modules.add(name.split(".0.")[-1].split(".")[0])
+        elif ".1." in name:  # MoD starts from layer 1
+            hidden_modules.add(name.split(".1.")[-1].split(".")[0])
+
+        if re.search(r"\.\d+\.", name) is None:
+            non_hidden_modules.add(name.split(".")[-2])  # remove weight/bias
+
+    trainable_layers = []
+    for module_name in finetuning_args.freeze_trainable_modules:
+        if module_name != "all" and module_name not in hidden_modules:
+            raise ValueError(
+                "Module {} is not found, please choose from {}".format(module_name, ", ".join(hidden_modules))
+            )
+
+        for idx in trainable_layer_ids:
+            trainable_layers.append(".{:d}.{}".format(idx, module_name if module_name != "all" else ""))
+
+    if finetuning_args.freeze_extra_modules:
+        for module_name in finetuning_args.freeze_extra_modules:
+            if module_name not in non_hidden_modules:
+                raise ValueError(
+                    "Module {} is not found, please choose from {}".format(module_name, ", ".join(non_hidden_modules))
+                )
+
+            trainable_layers.append(module_name)
+
+    model_type = getattr(model.config, "model_type", None)
+    if not finetuning_args.freeze_multi_modal_projector and model_type in COMPOSITE_MODELS:
+        trainable_layers.append(COMPOSITE_MODELS[model_type].projector_key)
+
+    forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
+    for name, param in model.named_parameters():
+        if any(trainable_layer in name for trainable_layer in trainable_layers) and not any(
+            forbidden_module in name for forbidden_module in forbidden_modules
+        ):
+            if cast_trainable_params_to_fp32:
+                param.data = param.data.to(torch.float32)
+        else:
+            param.requires_grad_(False)
+
+    logger.info_rank0("Set trainable layers: {}".format(",".join(trainable_layers)))
+
+
+def _setup_lora_tuning(
+    config: "PretrainedConfig",
+    model: "PreTrainedModel",
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
+    cast_trainable_params_to_fp32: bool,
+) -> "PeftModel":
+    if is_trainable:
+        logger.info_rank0("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
+
+    adapter_to_resume = None
+
+    if model_args.adapter_name_or_path is not None:
+        is_mergeable = True
+        if getattr(model, "quantization_method", None):  # merge lora in quantized model is unstable
+            assert len(model_args.adapter_name_or_path) == 1, "Quantized model only accepts a single adapter."
+            is_mergeable = False
+
+        if is_deepspeed_zero3_enabled():
+            assert len(model_args.adapter_name_or_path) == 1, "Cannot use multiple adapters in DeepSpeed ZeRO-3."
+            is_mergeable = False
+
+        if model_args.use_unsloth:
+            assert len(model_args.adapter_name_or_path) == 1, "Unsloth model only accepts a single adapter."
+            is_mergeable = False
+
+        if (is_trainable and not finetuning_args.create_new_adapter) or (not is_mergeable):
+            adapter_to_merge = model_args.adapter_name_or_path[:-1]
+            adapter_to_resume = model_args.adapter_name_or_path[-1]
+        else:
+            adapter_to_merge = model_args.adapter_name_or_path
+
+        init_kwargs = {
+            "subfolder": model_args.adapter_folder,
+            "offload_folder": model_args.offload_folder,
+            "cache_dir": model_args.cache_dir,
+            "revision": model_args.model_revision,
+            "token": model_args.hf_hub_token,
+        }
+
+        for adapter in adapter_to_merge:
+            model: LoraModel = PeftModel.from_pretrained(model, adapter, **init_kwargs)
+            model = model.merge_and_unload()
+
+        if len(adapter_to_merge) > 0:
+            logger.info_rank0(f"Merged {len(adapter_to_merge)} adapter(s).")
+
+        if adapter_to_resume is not None:  # resume lora training
+            if model_args.use_unsloth:
+                model = load_unsloth_peft_model(config, model_args, finetuning_args, is_trainable=is_trainable)
+            else:
+                model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
+
+        logger.info_rank0("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
+
+    if is_trainable and adapter_to_resume is None:  # create new lora weights while training
+        if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
+            target_modules = find_all_linear_modules(model, finetuning_args.freeze_vision_tower)
+        else:
+            target_modules = finetuning_args.lora_target
+
+        if finetuning_args.use_llama_pro:
+            target_modules = find_expanded_modules(model, target_modules, finetuning_args.freeze_trainable_layers)
+
+        target_modules = patch_target_modules(model, finetuning_args, target_modules)
+
+        if (
+            finetuning_args.use_dora
+            and getattr(model, "quantization_method", None) is not None
+            and getattr(model, "quantization_method", None) != QuantizationMethod.BNB
+        ):
+            raise ValueError("DoRA is not compatible with PTQ-quantized models.")
+
+        if model_args.resize_vocab and finetuning_args.additional_target is None:
+            input_embeddings = model.get_input_embeddings()
+            output_embeddings = model.get_output_embeddings()
+            module_names = set()
+            for name, module in model.named_modules():
+                if module in [input_embeddings, output_embeddings]:
+                    module_names.add(name.split(".")[-1])
+
+            finetuning_args.additional_target = module_names
+            logger.warning_rank0("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
+
+        peft_kwargs = {
+            "r": finetuning_args.lora_rank,
+            "target_modules": target_modules,
+            "lora_alpha": finetuning_args.lora_alpha,
+            "lora_dropout": finetuning_args.lora_dropout,
+            "use_rslora": finetuning_args.use_rslora,
+            "use_dora": finetuning_args.use_dora,
+            "modules_to_save": finetuning_args.additional_target,
+        }
+
+        if model_args.use_unsloth:
+            model = get_unsloth_peft_model(model, model_args, peft_kwargs)
+        else:
+            if finetuning_args.pissa_init:
+                if finetuning_args.pissa_iter == -1:
+                    logger.info_rank0("Using PiSSA initialization.")
+                    peft_kwargs["init_lora_weights"] = "pissa"
+                else:
+                    logger.info_rank0(f"Using PiSSA initialization with FSVD steps {finetuning_args.pissa_iter}.")
+                    peft_kwargs["init_lora_weights"] = f"pissa_niter_{finetuning_args.pissa_iter}"
+
+            lora_config = LoraConfig(
+                task_type=TaskType.CAUSAL_LM,
+                inference_mode=False,
+                **peft_kwargs,
+            )
+            model = get_peft_model(model, lora_config)
+
+    if is_trainable and cast_trainable_params_to_fp32:
+        for param in filter(lambda p: p.requires_grad, model.parameters()):
+            param.data = param.data.to(torch.float32)
+
+    return model
+
+
+def init_adapter(
+    config: "PretrainedConfig",
+    model: "PreTrainedModel",
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
+) -> "PreTrainedModel":
+    r"""Initialize the adapters.
+
+    Support full-parameter, freeze and LoRA training.
+
+    Note that the trainable parameters must be cast to float32.
+    """
+    if is_trainable and getattr(model, "quantization_method", None) is not None:
+        if finetuning_args.finetuning_type != "lora":
+            raise ValueError("Quantized models can only be used for the LoRA tuning.")
+
+        if finetuning_args.pissa_init:
+            raise ValueError("Cannot initialize PiSSA adapter on quantized models.")
+
+    # cast trainable parameters to float32 if:
+    # 1. is_trainable and not pure_bf16 and not badam and quantization_bit is not None (qlora)
+    # 2. is_trainable and not pure_bf16 and not badam and not zero3 (zero3 already in fp32)
+    cast_trainable_params_to_fp32 = False
+    if not is_trainable:
+        pass
+    elif finetuning_args.pure_bf16 or finetuning_args.use_badam:
+        logger.info_rank0("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
+    elif model_args.quantization_bit is None and is_deepspeed_zero3_enabled():
+        logger.info_rank0("DeepSpeed ZeRO3 detected, remaining trainable params in float32.")
+    else:
+        logger.info_rank0("Upcasting trainable params to float32.")
+        cast_trainable_params_to_fp32 = True
+
+    if finetuning_args.finetuning_type == "full":
+        _setup_full_tuning(model, finetuning_args, is_trainable, cast_trainable_params_to_fp32)
+    elif finetuning_args.finetuning_type == "freeze":
+        _setup_freeze_tuning(model, finetuning_args, is_trainable, cast_trainable_params_to_fp32)
+    elif finetuning_args.finetuning_type == "lora":
+        model = _setup_lora_tuning(
+            config, model, model_args, finetuning_args, is_trainable, cast_trainable_params_to_fp32
+        )
+    else:
+        raise NotImplementedError(f"Unknown finetuning type: {finetuning_args.finetuning_type}.")
+
+    return model