[Model] Support `ArcticForCausalLM` architecture (Snowflake/snowflake-arctic-instruct) (#5078)

Co-authored-by: vincent-4 <vincentzhongy+githubvincent4@gmail.com>

docs/supported_models/generative_models.md

@@ -28,6 +28,7 @@ python3 -m sglang.launch_server \
 | **Command-R** (Cohere)              | `CohereForAI/c4ai-command-r-v01`                 | Cohere’s open conversational LLM (Command series) optimized for long context, retrieval-augmented generation, and tool use. |
 | **DBRX** (Databricks)              | `databricks/dbrx-instruct`                       | Databricks’ 132B-parameter MoE model (36B active) trained on 12T tokens; competes with GPT-3.5 quality as a fully open foundation model. |
 | **Grok** (xAI)                     | `xai-org/grok-1`                                | xAI’s grok-1 model known for vast size(314B parameters) and high quality; integrated in SGLang for high-performance inference. |
+| **Arctic** (Snowflake)               | `Snowflake/snowflake-arctic-instruct`            | Snowflake’s dense-MoE model (17B active, 480B total) with top-2 routing, built for enterprise-grade reasoning, code, and instruction tasks. |
 | **ChatGLM** (GLM-130B family)       | `THUDM/chatglm2-6b`                              | Zhipu AI’s bilingual chat model (6B) excelling at Chinese-English dialogue; fine-tuned for conversational quality and alignment. |
 | **InternLM 2** (7B, 20B)           | `internlm/internlm2-7b`                          | Next-gen InternLM (7B and 20B) from SenseTime, offering strong reasoning and ultra-long context support (up to 200K tokens). |
 | **ExaONE 3** (Korean-English)      | `LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct`           | LG AI Research’s Korean-English model (7.8B) trained on 8T tokens; provides high-quality bilingual understanding and generation. |

python/sglang/srt/configs/__init__.py

+from sglang.srt.configs.arctic import ArcticConfig
 from sglang.srt.configs.chatglm import ChatGLMConfig
 from sglang.srt.configs.dbrx import DbrxConfig
 from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config
@@ -5,6 +6,7 @@ from sglang.srt.configs.exaone import ExaoneConfig
 from sglang.srt.configs.janus_pro import MultiModalityConfig
 
 __all__ = [
+    "ArcticConfig",
     "ExaoneConfig",
     "ChatGLMConfig",
     "DbrxConfig",

python/sglang/srt/configs/arctic.py

+# SPDX-License-Identifier: Apache-2.0
+
+"""Arctic model configuration"""
+
+from typing import Any, Dict, Optional
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json",
+}
+
+
+class ArcticConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an
+    Arctic model according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ArcticModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to root per-token, can be also interpreted as the `top-p` routing parameter
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Number of experts per Sparse MLP layer.
+        moe_layer_frequency (`int`, *optional*, defaults to 2):
+            Frequency of MoE layers in the model.
+    """
+
+    model_type = "arctic"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=1,
+        num_local_experts=8,
+        moe_layer_frequency=2,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.moe_layer_frequency = moe_layer_frequency
+
+        # For backward compatibility
+        self._attn_implementation = kwargs.pop("_attn_implementation", "eager")
+        self.use_residual = kwargs.pop("use_residual", True)
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

python/sglang/srt/hf_transformers_utils.py

@@ -31,6 +31,7 @@ from transformers import (
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
 
 from sglang.srt.configs import (
+    ArcticConfig,
     ChatGLMConfig,
     DbrxConfig,
     DeepseekVL2Config,
@@ -41,6 +42,7 @@ from sglang.srt.connector import create_remote_connector
 from sglang.srt.utils import is_remote_url
 
 _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
+    ArcticConfig.model_type: ArcticConfig,
     ChatGLMConfig.model_type: ChatGLMConfig,
     DbrxConfig.model_type: DbrxConfig,
     ExaoneConfig.model_type: ExaoneConfig,

python/sglang/srt/models/arctic.py

+# Copyright 2023-2025 SGLang 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.
+# ==============================================================================
+# Copyright 2024 Cohere and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+# ==============================================================================
+
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/arctic.py
+
+"""Inference-only Snowflake Arctic model."""
+
+import logging
+from typing import Iterable, List, Optional, Set, Tuple, Union
+
+import torch
+from torch import nn
+
+from sglang.srt.configs.arctic import ArcticConfig
+from sglang.srt.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.fused_moe import fused_experts, fused_topk
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
+from sglang.srt.layers.quantization import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_executor.utils import set_weight_attrs
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.platforms import current_platform
+
+from .interfaces import SupportsPP, SupportsQuant
+from .utils import (
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class ArcticMLP(nn.Module):
+    def __init__(
+        self,
+        config: ArcticConfig,
+        expert_id: int = -1,
+        is_residual_mlp: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.expert_id = expert_id
+
+        self.ffn_dim = (
+            config.intermediate_size if not is_residual_mlp else self.hidden_size
+        )
+
+        self.w13 = MergedColumnParallelLinear(
+            self.hidden_size, [self.ffn_dim] * 2, bias=False, quant_config=quant_config
+        )
+        self.w2 = RowParallelLinear(
+            self.ffn_dim,
+            self.hidden_size,
+            bias=False,
+            reduce_results=reduce_results,
+            quant_config=quant_config,
+        )
+        if config.hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {config.hidden_act}. "
+                "Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, hidden_states):
+        gate_up, _ = self.w13(hidden_states)
+        hidden_states = self.act_fn(gate_up)
+        hidden_states, _ = self.w2(hidden_states)
+        return hidden_states
+
+
+class ArcticMoE(nn.Module):
+    """
+    Model-parallel implementation of Arctic MoE Layer.
+    """
+
+    def __init__(
+        self,
+        config: ArcticConfig,
+        tp_size: Optional[int] = None,
+        params_dtype: Optional[torch.dtype] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        layer_id = extract_layer_index(prefix)
+        self.tp_size = tp_size or get_tensor_model_parallel_world_size()
+        self.hidden_size = config.hidden_size
+        self.num_experts = config.num_local_experts
+        self.layer_id = layer_id
+        self.top_k = config.num_experts_per_tok
+        self.intermediate_size = config.intermediate_size // self.tp_size
+
+        self.is_moe_layer = (layer_id + 1) % config.moe_layer_frequency == 0
+        self.is_quant = quant_config is not None
+        self.reduce_results = reduce_results
+        # Some other parameters
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
+
+        if not self.is_moe_layer:
+            self.mlp = ArcticMLP(
+                config,
+                quant_config=quant_config,
+                reduce_results=reduce_results,
+                prefix=f"{prefix}.mlp",
+            )
+        else:
+            self.gate = ReplicatedLinear(
+                self.hidden_size,
+                self.num_experts,
+                bias=False,
+                params_dtype=self.params_dtype,
+                quant_config=quant_config,
+                prefix=f"{prefix}.gate",
+            )
+            if self.is_quant:
+                raise NotImplementedError("Quantization is not supported yet.")
+            else:
+                self.ws = nn.Parameter(
+                    torch.empty(
+                        self.num_experts,
+                        2 * self.intermediate_size,
+                        self.hidden_size,
+                        device=current_platform.device_type,
+                        dtype=self.params_dtype,
+                    )
+                )
+                self.w2s = nn.Parameter(
+                    torch.empty(
+                        self.num_experts,
+                        self.hidden_size,
+                        self.intermediate_size,
+                        device=current_platform.device_type,
+                        dtype=self.params_dtype,
+                    )
+                )
+            set_weight_attrs(
+                self.ws,
+                {
+                    "weight_loader": self.weight_loader,
+                },
+            )
+            set_weight_attrs(
+                self.w2s,
+                {
+                    "weight_loader": self.weight_loader,
+                },
+            )
+
+    def weight_loader(
+        self,
+        param: nn.Parameter,
+        loaded_weight: torch.Tensor,
+        weight_name: str,
+        expert_id: int,
+    ):
+        tp_rank = get_tensor_model_parallel_rank()
+        param_data = param.data
+        shard_size = self.intermediate_size
+        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+        if weight_name.endswith("w1.weight"):
+            param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
+        if weight_name.endswith("w3.weight"):
+            param_data[expert_id, shard_size : 2 * shard_size, :] = loaded_weight[
+                shard, :
+            ]
+        if weight_name.endswith("w2.weight"):
+            param_data[expert_id, :, :] = loaded_weight[:, shard]
+
+    def local_moe_fused(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        do_normalize = self.top_k > 1
+        topk_weights, topk_ids = fused_topk(
+            hidden_states, router_logits, self.top_k, renormalize=do_normalize
+        )
+        # topk_ids: (num_tokens, k)
+        if self.is_quant:
+            raise NotImplementedError("Quantization is not supported yet.")
+            # if 2 * num_tokens <= self.num_experts:
+            #     # If much fewer tokens than experts, use selective dequantize.
+            #     ws_dequantized = self.ws.ds_selective_dequantize(topk_ids.flatten())
+            #     w2s_dequantized = self.w2s.ds_selective_dequantize(topk_ids.flatten())
+            #     # We gathered the experts to the tokens so update the mapping.
+            #     topk_ids = torch.arange(
+            #         0,
+            #         topk_ids.numel(),
+            #         device=topk_ids.device,
+            #     ).reshape(topk_ids.shape)
+            # else:
+            #     ws_dequantized = self.ws.ds_dequantize()
+            #     w2s_dequantized = self.w2s.ds_dequantize()
+
+        final_hidden_states = fused_experts(
+            hidden_states,
+            self.ws,
+            self.w2s,
+            topk_weights,
+            topk_ids,
+            inplace=True,
+        )
+        if self.reduce_results and self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
+        return final_hidden_states.view(num_tokens, hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor):
+        if self.is_moe_layer:
+            final_hidden_states = self.local_moe_fused(hidden_states)
+        else:
+            final_hidden_states = self.mlp(hidden_states)
+        return final_hidden_states
+
+
+class ArcticAttention(nn.Module):
+    def __init__(
+        self,
+        config: ArcticConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        layer_idx = extract_layer_index(prefix)
+
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=False,
+            reduce_results=True,
+            quant_config=quant_config,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_idx,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, forward_batch)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class ArcticDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: ArcticConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        layer_idx = extract_layer_index(prefix)
+        is_moe_layer = (layer_idx + 1) % config.moe_layer_frequency == 0
+        self.use_residual = config.use_residual and is_moe_layer
+        self.self_attn = ArcticAttention(
+            config, quant_config=quant_config, prefix=f"{prefix}.self_attn"
+        )
+        self.block_sparse_moe = ArcticMoE(
+            config,
+            quant_config=quant_config,
+            reduce_results=(not self.use_residual),
+            prefix=f"{prefix}.block_sparse_moe",
+        )
+
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        if self.use_residual:
+            self.residual_layernorm = RMSNorm(
+                config.hidden_size, eps=config.rms_norm_eps
+            )
+            self.residual_mlp = ArcticMLP(
+                config,
+                is_residual_mlp=True,
+                reduce_results=False,
+                prefix=f"{prefix}.residual_mlp",
+            )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        residual_input = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+        hidden_states = residual_input + hidden_states
+
+        residual_attn = hidden_states
+        if self.use_residual:
+            hidden_states = self.residual_layernorm(hidden_states)
+            hidden_states = self.residual_mlp(hidden_states)
+            residual_mlp = hidden_states
+            hidden_states = self.post_attention_layernorm(residual_input)
+            hidden_states = self.block_sparse_moe(hidden_states)
+            hidden_states = residual_mlp + hidden_states
+            hidden_states = tensor_model_parallel_all_reduce(hidden_states)
+            hidden_states = residual_attn + hidden_states
+        else:
+            hidden_states = self.post_attention_layernorm(hidden_states)
+            hidden_states = self.block_sparse_moe(hidden_states)
+            hidden_states = residual_attn + hidden_states
+        return hidden_states
+
+
+class ArcticModel(nn.Module):
+    def __init__(
+        self,
+        *,
+        config: ArcticConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size, config.hidden_size, org_num_embeddings=self.vocab_size
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: ArcticDecoderLayer(config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.hidden_size
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if input_embeds is not None:
+            hidden_states = input_embeds
+        else:
+            hidden_states = self.get_input_embeddings(input_ids)
+
+        for layer in self.layers[self.start_layer : self.end_layer]:
+            hidden_states = layer(positions, hidden_states, forward_batch)
+
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class ArcticForCausalLM(nn.Module):
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
+
+    def __init__(
+        self,
+        *,
+        config: ArcticConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.supports_torch_tp = True
+        self.model = ArcticModel(
+            config=config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "model"),
+        )
+        self.vocab_size = config.vocab_size
+        self.lm_head = ParallelLMHead(
+            self.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.num_experts = config.num_local_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+        self.unpadded_vocab_size = config.vocab_size
+        self.logits_processor = LogitsProcessor(self.config)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: Optional[torch.Tensor] = None,
+    ) -> LogitsProcessorOutput:
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        mlp_params_mapping: List[Tuple[str, str, int]] = []
+        expert_params_mapping: List[Tuple[str, str, int]] = []
+        num_layers = self.config.num_hidden_layers
+
+        for layer in range(num_layers):
+            mlp_params_mapping.append(
+                (
+                    f"layers.{layer}.residual_mlp.w13.weight",
+                    f"layers.{layer}.residual_mlp.w1.weight",
+                    0,
+                )
+            )
+            mlp_params_mapping.append(
+                (
+                    f"layers.{layer}.residual_mlp.w13.weight",
+                    f"layers.{layer}.residual_mlp.w3.weight",
+                    1,
+                )
+            )
+            if (layer + 1) % self.config.moe_layer_frequency != 0:
+                # MLP layers
+                mlp_params_mapping.append(
+                    (
+                        f"layers.{layer}.block_sparse_moe.mlp.w13.weight",
+                        f"layers.{layer}.block_sparse_moe.mlp.w1.weight",
+                        0,
+                    )
+                )
+                mlp_params_mapping.append(
+                    (
+                        f"layers.{layer}.block_sparse_moe.mlp.w13.weight",
+                        f"layers.{layer}.block_sparse_moe.mlp.w3.weight",
+                        1,
+                    )
+                )
+            else:
+                # MoE layers
+                for expert_id in range(self.config.num_local_experts):
+                    expert_params_mapping.append(
+                        ("ws", f"experts.{expert_id}.w1.weight", expert_id)
+                    )
+                    expert_params_mapping.append(
+                        ("w2s", f"experts.{expert_id}.w2.weight", expert_id)
+                    )
+                    expert_params_mapping.append(
+                        ("ws", f"experts.{expert_id}.w3.weight", expert_id)
+                    )
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+
+        logger.info(
+            "It will take ~10 minutes loading from the 16-bit weights. "
+            "Alternatively, use the prequantized 8-bit weights of arctic "
+            "and set load-format to `sharded_state` will accelerate loading."
+        )
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for param_name, weight_name, shard_id in mlp_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id)
+                    break
+                else:
+                    for param_name, weight_name, shard_id in expert_params_mapping:
+                        if weight_name not in name:
+                            continue
+                        name = name.replace(weight_name, param_name)
+                        if is_pp_missing_parameter(name, self):
+                            continue
+                        param = params_dict[name]
+                        weight_loader = param.weight_loader
+                        weight_loader(
+                            param, loaded_weight, weight_name, expert_id=shard_id
+                        )
+                        break
+                    else:
+                        if name.endswith(".bias") and name not in params_dict:
+                            continue
+                        if is_pp_missing_parameter(name, self):
+                            continue
+                        param = params_dict[name]
+
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params