Revert "[Model] Support `ArcticForCausalLM` architecture...

Revert "[Model] Support `ArcticForCausalLM` architecture (Snowflake/snowflake-arctic-instruct)" (#5754)

docs/supported_models/generative_models.md

@@ -28,7 +28,6 @@ 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
@@ -6,7 +5,6 @@ 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

@@ -31,7 +31,6 @@ 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,
@@ -42,7 +41,6 @@ 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,