wehub-resource-sync
751 lines
25 KiB
Python
751 lines
25 KiB
Python
"""A centralized registry of all existing model architures and their configurations."""
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import dataclasses
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from typing import Any, Callable, Dict, Literal, Optional, Tuple # noqa: UP035
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from tvm.relax.frontend import nn
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from mlc_llm.loader import ExternMapping, QuantizeMapping
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from mlc_llm.quantization import make_quantization_functions
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from mlc_llm.quantization.quantization import Quantization
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from .baichuan import baichuan_loader, baichuan_model
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from .bert import bert_loader, bert_model
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from .chatglm3 import chatglm3_loader, chatglm3_model
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from .cohere import cohere_loader, cohere_model
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from .deepseek import deepseek_loader, deepseek_model
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from .deepseek_v2 import deepseek_v2_loader, deepseek_v2_model
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from .eagle import eagle_loader, eagle_model
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from .gemma import gemma_loader, gemma_model
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from .gemma2 import gemma2_loader, gemma2_model
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from .gemma3 import gemma3_loader, gemma3_model
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from .gpt2 import gpt2_loader, gpt2_model
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from .gpt_bigcode import gpt_bigcode_loader, gpt_bigcode_model
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from .gpt_j import gpt_j_loader, gpt_j_model
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from .gpt_neox import gpt_neox_loader, gpt_neox_model
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from .internlm import internlm_loader, internlm_model
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from .internlm2 import internlm2_loader, internlm2_model
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from .llama import llama_loader, llama_model
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from .llama4 import llama4_loader, llama4_model
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from .llava import llava_loader, llava_model
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from .medusa import medusa_loader, medusa_model
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from .minicpm import minicpm_loader, minicpm_model
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from .ministral3 import ministral3_loader, ministral3_model
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from .mistral import mistral_loader, mistral_model
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from .mixtral import mixtral_loader, mixtral_model
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from .nemotron import nemotron_loader, nemotron_model
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from .olmo import olmo_loader, olmo_model
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from .olmo2 import olmo2_loader, olmo2_model
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from .orion import orion_loader, orion_model
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from .phi import phi_loader, phi_model
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from .phi3 import phi3_loader, phi3_model
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from .phi3v import phi3v_loader, phi3v_model
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from .qwen import qwen_loader, qwen_model
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from .qwen2 import qwen2_loader, qwen2_model
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from .qwen2_moe import qwen2_moe_loader, qwen2_moe_model
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from .qwen3 import qwen3_loader, qwen3_model
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from .qwen3_moe import qwen3_moe_loader, qwen3_moe_model
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from .qwen35 import qwen35_loader, qwen35_model
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from .rwkv5 import rwkv5_loader, rwkv5_model
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from .rwkv6 import rwkv6_loader, rwkv6_model
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from .stable_lm import stablelm_loader, stablelm_model
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from .starcoder2 import starcoder2_loader, starcoder2_model
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ModelConfig = Any
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"""A ModelConfig is an object that represents a model architecture. It is required to have
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a class method `from_file` with the following signature:
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def from_file(cls, path: Path) -> ModelConfig:
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...
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"""
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FuncGetExternMap = Callable[[ModelConfig, Quantization], ExternMapping]
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FuncQuantization = Callable[[ModelConfig, Quantization], Tuple[nn.Module, QuantizeMapping]] # noqa: UP006
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@dataclasses.dataclass
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class EmbeddingMetadata:
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"""Embedding model metadata.
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Parameters
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----------
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model_type: Literal["encoder", "decoder"]
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The type of the embedding model.
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pooling_strategy: Literal["cls", "mean", "last"]
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The pooling strategy to use for the embedding model.
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normalize: bool = True
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Default to normalize the embedding.
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"""
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model_type: Literal["encoder", "decoder"]
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pooling_strategy: Literal["cls", "mean", "last"]
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normalize: bool = True
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@dataclasses.dataclass
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class Model:
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"""All about a model architecture: its configuration, its parameter loader and quantization.
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Parameters
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----------
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name : str
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The name of the model.
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model : Callable[[ModelConfig], nn.Module]
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A method that creates the `nn.Module` that represents the model from `ModelConfig`.
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config : ModelConfig
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A class that has a `from_file` class method, whose signature is "Path -> ModelConfig".
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source : Dict[str, FuncGetExternMap]
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A dictionary that maps the name of a source format to parameter mapping.
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quantize: Dict[str, FuncQuantization]
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A dictionary that maps the name of a quantization method to quantized model and the
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quantization parameter mapping.
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model_task: Literal["chat", "embedding"] = "chat"
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A task of the model to distinguish between chat and embedding models. Default to "chat".
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embedding_metadata: Optional[EmbeddingMetadata] = None
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Metadata for the embedding model. Default to None.
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"""
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name: str
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config: ModelConfig
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model: Callable[[ModelConfig], nn.Module]
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source: Dict[str, FuncGetExternMap] # noqa: UP006
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quantize: Dict[str, FuncQuantization] # noqa: UP006
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model_task: Literal["chat", "embedding"] = "chat"
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embedding_metadata: Optional[EmbeddingMetadata] = None
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def __post_init__(self):
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if self.model_task == "embedding" and self.embedding_metadata is None:
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raise ValueError(f"[Model] {self.name}: Embedding model must have embedding metadata.")
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if self.model_task == "chat" and self.embedding_metadata is not None:
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raise ValueError(
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f"[Model] {self.name}: Chat model not expected to have embedding metadata."
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)
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MODELS: Dict[str, Model] = { # noqa: UP006
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"llama": Model(
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name="llama",
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model=llama_model.LlamaForCausalLM,
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config=llama_model.LlamaConfig,
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source={
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"huggingface-torch": llama_loader.huggingface,
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"huggingface-safetensor": llama_loader.huggingface,
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"awq": llama_loader.awq,
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},
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quantize=make_quantization_functions(
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llama_model.LlamaForCausalLM,
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supports_awq=True,
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supports_per_tensor=True,
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),
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),
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"llama4": Model(
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name="llama4",
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model=llama4_model.Llama4ForCausalLM,
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config=llama4_model.Llama4Config,
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source={
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"huggingface-torch": llama4_loader.huggingface,
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"huggingface-safetensor": llama4_loader.huggingface,
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},
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quantize=make_quantization_functions(
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llama4_model.Llama4ForCausalLM,
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supports_per_tensor=True,
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),
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),
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"mistral": Model(
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name="mistral",
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model=mistral_model.MistralForCausalLM,
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config=mistral_model.MistralConfig,
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source={
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"huggingface-torch": mistral_loader.huggingface,
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"huggingface-safetensor": mistral_loader.huggingface,
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"awq": mistral_loader.awq,
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},
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quantize=make_quantization_functions(
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mistral_model.MistralForCausalLM,
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),
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),
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"ministral3": Model(
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name="ministral3",
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model=ministral3_model.Mistral3ForConditionalGeneration,
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config=ministral3_model.Ministral3Config,
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source={
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"huggingface-torch": ministral3_loader.huggingface,
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"huggingface-safetensor": ministral3_loader.huggingface,
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},
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quantize=make_quantization_functions(
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ministral3_model.Mistral3ForConditionalGeneration,
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supports_block_scale=True,
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),
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),
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"gemma": Model(
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name="gemma",
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model=gemma_model.GemmaForCausalLM,
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config=gemma_model.GemmaConfig,
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source={
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"huggingface-torch": gemma_loader.huggingface,
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"huggingface-safetensor": gemma_loader.huggingface,
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},
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quantize=make_quantization_functions(
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gemma_model.GemmaForCausalLM,
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supports_ft_quant=False,
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),
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),
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"gemma2": Model(
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name="gemma2",
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model=gemma2_model.Gemma2ForCausalLM,
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config=gemma2_model.Gemma2Config,
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source={
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"huggingface-torch": gemma2_loader.huggingface,
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"huggingface-safetensor": gemma2_loader.huggingface,
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},
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quantize=make_quantization_functions(
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gemma2_model.Gemma2ForCausalLM,
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supports_ft_quant=False,
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),
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),
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"gemma3": Model(
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name="gemma3",
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model=gemma3_model.Gemma3ForCausalLM,
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config=gemma3_model.Gemma3Config,
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source={
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"huggingface-torch": gemma3_loader.huggingface,
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"huggingface-safetensor": gemma3_loader.huggingface,
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},
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quantize=make_quantization_functions(
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gemma3_model.Gemma3ForCausalLM,
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supports_ft_quant=False,
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),
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),
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"gemma3_text": Model(
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name="gemma3_text",
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model=gemma3_model.Gemma3ForCausalLM,
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config=gemma3_model.Gemma3Config,
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source={
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"huggingface-torch": gemma3_loader.huggingface,
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"huggingface-safetensor": gemma3_loader.huggingface,
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},
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quantize=make_quantization_functions(
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gemma3_model.Gemma3ForCausalLM,
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supports_ft_quant=False,
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),
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),
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"gpt2": Model(
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name="gpt2",
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model=gpt2_model.GPT2LMHeadModel,
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config=gpt2_model.GPT2Config,
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source={
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"huggingface-torch": gpt2_loader.huggingface,
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"huggingface-safetensor": gpt2_loader.huggingface,
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},
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quantize=make_quantization_functions(
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gpt2_model.GPT2LMHeadModel,
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),
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),
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"mixtral": Model(
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name="mixtral",
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model=mixtral_model.MixtralForCausalLM,
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config=mixtral_model.MixtralConfig,
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source={
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"huggingface-torch": mixtral_loader.huggingface,
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"huggingface-safetensor": mixtral_loader.huggingface,
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},
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quantize=make_quantization_functions(
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mixtral_model.MixtralForCausalLM,
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supports_awq=True,
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awq_unsupported_message="AWQ is not implemented for Mixtral models.",
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supports_per_tensor=True,
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),
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),
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"gpt_neox": Model(
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name="gpt_neox",
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model=gpt_neox_model.GPTNeoXForCausalLM,
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config=gpt_neox_model.GPTNeoXConfig,
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source={
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"huggingface-torch": gpt_neox_loader.huggingface,
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"huggingface-safetensor": gpt_neox_loader.huggingface,
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},
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quantize=make_quantization_functions(
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gpt_neox_model.GPTNeoXForCausalLM,
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),
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),
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"gpt_bigcode": Model(
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name="gpt_bigcode",
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model=gpt_bigcode_model.GPTBigCodeForCausalLM,
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config=gpt_bigcode_model.GPTBigCodeConfig,
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source={
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"huggingface-torch": gpt_bigcode_loader.huggingface,
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"huggingface-safetensor": gpt_bigcode_loader.huggingface,
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},
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quantize=make_quantization_functions(
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gpt_bigcode_model.GPTBigCodeForCausalLM,
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),
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),
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"phi-msft": Model(
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name="phi-msft",
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model=phi_model.PhiForCausalLM,
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config=phi_model.PhiConfig,
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source={
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"huggingface-torch": phi_loader.huggingface,
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"huggingface-safetensor": phi_loader.huggingface,
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},
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quantize=make_quantization_functions(
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phi_model.PhiForCausalLM,
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),
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),
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"phi": Model(
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name="phi",
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model=phi_model.PhiForCausalLM,
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config=phi_model.Phi1Config,
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source={
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"huggingface-torch": phi_loader.phi1_huggingface,
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"huggingface-safetensor": phi_loader.phi1_huggingface,
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},
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quantize=make_quantization_functions(
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phi_model.PhiForCausalLM,
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),
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),
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"phi3": Model(
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name="phi3",
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model=phi3_model.Phi3ForCausalLM,
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config=phi3_model.Phi3Config,
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source={
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"huggingface-torch": phi3_loader.phi3_huggingface,
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"huggingface-safetensor": phi3_loader.phi3_huggingface,
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},
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quantize=make_quantization_functions(
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phi3_model.Phi3ForCausalLM,
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),
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),
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"phi3_v": Model(
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name="phi3_v",
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model=phi3v_model.Phi3VForCausalLM,
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config=phi3v_model.Phi3VConfig,
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source={
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"huggingface-torch": phi3v_loader.huggingface,
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"huggingface-safetensor": phi3v_loader.huggingface,
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},
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quantize=make_quantization_functions(
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phi3v_model.Phi3VForCausalLM,
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),
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),
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"qwen": Model(
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name="qwen",
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model=qwen_model.QWenLMHeadModel,
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config=qwen_model.QWenConfig,
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source={
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"huggingface-torch": qwen_loader.huggingface,
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"huggingface-safetensor": qwen_loader.huggingface,
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},
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quantize=make_quantization_functions(
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qwen_model.QWenLMHeadModel,
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),
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),
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"qwen2": Model(
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name="qwen2",
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model=qwen2_model.QWen2LMHeadModel,
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config=qwen2_model.QWen2Config,
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source={
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"huggingface-torch": qwen2_loader.huggingface,
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"huggingface-safetensor": qwen2_loader.huggingface,
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},
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quantize=make_quantization_functions(
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qwen2_model.QWen2LMHeadModel,
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),
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),
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"qwen2_moe": Model(
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name="qwen2_moe",
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model=qwen2_moe_model.Qwen2MoeForCausalLM,
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config=qwen2_moe_model.Qwen2MoeConfig,
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source={
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"huggingface-torch": qwen2_moe_loader.huggingface,
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"huggingface-safetensor": qwen2_moe_loader.huggingface,
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},
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quantize=make_quantization_functions(
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qwen2_moe_model.Qwen2MoeForCausalLM,
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),
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),
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"qwen3": Model(
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name="qwen3",
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model=qwen3_model.Qwen3LMHeadModel,
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config=qwen3_model.Qwen3Config,
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source={
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"huggingface-torch": qwen3_loader.huggingface,
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"huggingface-safetensor": qwen3_loader.huggingface,
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},
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quantize=make_quantization_functions(
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qwen3_model.Qwen3LMHeadModel,
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supports_block_scale=True,
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),
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),
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"qwen3-embedding": Model(
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name="qwen3-embedding",
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model=qwen3_model.Qwen3EmbeddingModel,
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config=qwen3_model.Qwen3Config,
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source={
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"huggingface-torch": qwen3_loader.huggingface_embedding,
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"huggingface-safetensor": qwen3_loader.huggingface_embedding,
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},
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quantize=make_quantization_functions(
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qwen3_model.Qwen3EmbeddingModel,
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supports_block_scale=True,
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),
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model_task="embedding",
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embedding_metadata=EmbeddingMetadata(
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model_type="decoder",
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pooling_strategy="last",
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normalize=True,
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),
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),
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"qwen3_5": Model(
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name="qwen3_5",
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model=qwen35_model.Qwen35LMHeadModel,
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config=qwen35_model.Qwen35Config,
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source={
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"huggingface-torch": qwen35_loader.huggingface,
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"huggingface-safetensor": qwen35_loader.huggingface,
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},
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quantize=make_quantization_functions(
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qwen35_model.Qwen35LMHeadModel,
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),
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),
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"qwen3_5_text": Model(
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name="qwen3_5_text",
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model=qwen35_model.Qwen35LMHeadModel,
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config=qwen35_model.Qwen35Config,
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source={
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"huggingface-torch": qwen35_loader.huggingface,
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"huggingface-safetensor": qwen35_loader.huggingface,
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},
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quantize=make_quantization_functions(
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qwen35_model.Qwen35LMHeadModel,
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),
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),
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"qwen3_moe": Model(
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name="qwen3_moe",
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model=qwen3_moe_model.Qwen3MoeForCausalLM,
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config=qwen3_moe_model.Qwen3MoeConfig,
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source={
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"huggingface-torch": qwen3_moe_loader.huggingface,
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"huggingface-safetensor": qwen3_moe_loader.huggingface,
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},
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quantize=make_quantization_functions(
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qwen3_moe_model.Qwen3MoeForCausalLM,
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supports_block_scale=True,
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),
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),
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"deepseek_v2": Model(
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name="deepseek_v2",
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model=deepseek_v2_model.DeepseekV2ForCausalLM,
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config=deepseek_v2_model.DeepseekV2Config,
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source={
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"huggingface-torch": deepseek_v2_loader.huggingface,
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"huggingface-safetensor": deepseek_v2_loader.huggingface,
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},
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quantize=make_quantization_functions(
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deepseek_v2_model.DeepseekV2ForCausalLM,
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),
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),
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"deepseek_v3": Model(
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name="deepseek_v3",
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model=deepseek_v2_model.DeepseekV2ForCausalLM,
|
|
config=deepseek_v2_model.DeepseekV2Config,
|
|
source={
|
|
"huggingface-torch": deepseek_v2_loader.huggingface,
|
|
"huggingface-safetensor": deepseek_v2_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
deepseek_v2_model.DeepseekV2ForCausalLM,
|
|
supports_block_scale=True,
|
|
),
|
|
),
|
|
"stablelm": Model(
|
|
name="stablelm",
|
|
model=stablelm_model.StableLmForCausalLM,
|
|
config=stablelm_model.StableLmConfig,
|
|
source={
|
|
"huggingface-torch": stablelm_loader.huggingface,
|
|
"huggingface-safetensor": stablelm_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
stablelm_model.StableLmForCausalLM,
|
|
),
|
|
),
|
|
"baichuan": Model(
|
|
name="baichuan",
|
|
model=baichuan_model.BaichuanForCausalLM,
|
|
config=baichuan_model.BaichuanConfig,
|
|
source={
|
|
"huggingface-torch": baichuan_loader.huggingface,
|
|
"huggingface-safetensor": baichuan_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
baichuan_model.BaichuanForCausalLM,
|
|
),
|
|
),
|
|
"internlm": Model(
|
|
name="internlm",
|
|
model=internlm_model.InternLMForCausalLM,
|
|
config=internlm_model.InternLMConfig,
|
|
source={
|
|
"huggingface-torch": internlm_loader.huggingface,
|
|
"huggingface-safetensor": internlm_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
internlm_model.InternLMForCausalLM,
|
|
),
|
|
),
|
|
"internlm2": Model(
|
|
name="internlm2",
|
|
model=internlm2_model.InternLM2ForCausalLM,
|
|
config=internlm2_model.InternLM2Config,
|
|
source={
|
|
"huggingface-torch": internlm2_loader.huggingface,
|
|
"huggingface-safetensor": internlm2_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
internlm2_model.InternLM2ForCausalLM,
|
|
),
|
|
),
|
|
"rwkv5": Model(
|
|
name="rwkv5",
|
|
model=rwkv5_model.RWKV5_ForCausalLM,
|
|
config=rwkv5_model.RWKV5Config,
|
|
source={
|
|
"huggingface-torch": rwkv5_loader.huggingface,
|
|
"huggingface-safetensor": rwkv5_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
rwkv5_model.RWKV5_ForCausalLM,
|
|
),
|
|
),
|
|
"orion": Model(
|
|
name="orion",
|
|
model=orion_model.OrionForCausalLM,
|
|
config=orion_model.OrionConfig,
|
|
source={
|
|
"huggingface-torch": orion_loader.huggingface,
|
|
"huggingface-safetensor": orion_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
orion_model.OrionForCausalLM,
|
|
supports_ft_quant=False,
|
|
),
|
|
),
|
|
"llava": Model(
|
|
name="llava",
|
|
model=llava_model.LlavaForCausalLM,
|
|
config=llava_model.LlavaConfig,
|
|
source={
|
|
"huggingface-torch": llava_loader.huggingface,
|
|
"huggingface-safetensor": llava_loader.huggingface,
|
|
"awq": llava_loader.awq,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
llava_model.LlavaForCausalLM,
|
|
supports_awq=True,
|
|
supports_ft_quant=False,
|
|
),
|
|
),
|
|
"rwkv6": Model(
|
|
name="rwkv6",
|
|
model=rwkv6_model.RWKV6_ForCausalLM,
|
|
config=rwkv6_model.RWKV6Config,
|
|
source={
|
|
"huggingface-torch": rwkv6_loader.huggingface,
|
|
"huggingface-safetensor": rwkv6_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
rwkv6_model.RWKV6_ForCausalLM,
|
|
supports_ft_quant=False,
|
|
),
|
|
),
|
|
"chatglm": Model(
|
|
name="chatglm",
|
|
model=chatglm3_model.ChatGLMForCausalLM,
|
|
config=chatglm3_model.GLMConfig,
|
|
source={
|
|
"huggingface-torch": chatglm3_loader.huggingface,
|
|
"huggingface-safetensor": chatglm3_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
chatglm3_model.ChatGLMForCausalLM,
|
|
supports_ft_quant=False,
|
|
),
|
|
),
|
|
"eagle": Model(
|
|
name="eagle",
|
|
model=eagle_model.EagleForCausalLM,
|
|
config=eagle_model.EagleConfig,
|
|
source={
|
|
"huggingface-torch": eagle_loader.huggingface,
|
|
"huggingface-safetensor": eagle_loader.huggingface,
|
|
"awq": eagle_loader.awq,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
eagle_model.EagleForCausalLM,
|
|
supports_awq=True,
|
|
),
|
|
),
|
|
"bert": Model(
|
|
name="bert",
|
|
model=bert_model.BertModel,
|
|
config=bert_model.BertConfig,
|
|
source={
|
|
"huggingface-torch": bert_loader.huggingface,
|
|
"huggingface-safetensor": bert_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
bert_model.BertModel,
|
|
),
|
|
model_task="embedding",
|
|
embedding_metadata=EmbeddingMetadata(
|
|
model_type="encoder",
|
|
pooling_strategy="cls",
|
|
normalize=True,
|
|
),
|
|
),
|
|
"medusa": Model(
|
|
name="medusa",
|
|
model=medusa_model.MedusaModel,
|
|
config=medusa_model.MedusaConfig,
|
|
source={
|
|
"huggingface-torch": medusa_loader.huggingface,
|
|
"huggingface-safetensor": medusa_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
medusa_model.MedusaModel,
|
|
supports_group_quant=False,
|
|
supports_ft_quant=False,
|
|
),
|
|
),
|
|
"starcoder2": Model(
|
|
name="starcoder2",
|
|
model=starcoder2_model.Starcoder2ForCausalLM,
|
|
config=starcoder2_model.Starcoder2Config,
|
|
source={
|
|
"huggingface-torch": starcoder2_loader.huggingface,
|
|
"huggingface-safetensor": starcoder2_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
starcoder2_model.Starcoder2ForCausalLM,
|
|
),
|
|
),
|
|
"cohere": Model(
|
|
name="cohere",
|
|
model=cohere_model.CohereForCausalLM,
|
|
config=cohere_model.CohereConfig,
|
|
source={
|
|
"huggingface-torch": cohere_loader.huggingface,
|
|
"huggingface-safetensor": cohere_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
cohere_model.CohereForCausalLM,
|
|
),
|
|
),
|
|
"minicpm": Model(
|
|
name="minicpm",
|
|
model=minicpm_model.MiniCPMForCausalLM,
|
|
config=minicpm_model.MiniCPMConfig,
|
|
source={
|
|
"huggingface-torch": minicpm_loader.huggingface,
|
|
"huggingface-safetensor": minicpm_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
minicpm_model.MiniCPMForCausalLM,
|
|
),
|
|
),
|
|
"deepseek": Model(
|
|
name="deepseek",
|
|
model=deepseek_model.DeepseekForCausalLM,
|
|
config=deepseek_model.DeepseekConfig,
|
|
source={
|
|
"huggingface-torch": deepseek_loader.huggingface,
|
|
"huggingface-safetensor": deepseek_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
deepseek_model.DeepseekForCausalLM,
|
|
),
|
|
),
|
|
"gptj": Model(
|
|
name="gptj",
|
|
model=gpt_j_model.GPTJForCausalLM,
|
|
config=gpt_j_model.GPTJConfig,
|
|
source={
|
|
"huggingface-torch": gpt_j_loader.huggingface,
|
|
"huggingface-safetensor": gpt_j_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
gpt_j_model.GPTJForCausalLM,
|
|
),
|
|
),
|
|
"olmo": Model(
|
|
name="olmo",
|
|
model=olmo_model.OLMoForCausalLM,
|
|
config=olmo_model.OLMoConfig,
|
|
source={
|
|
"huggingface-torch": olmo_loader.huggingface,
|
|
"huggingface-safetensor": olmo_loader.huggingface,
|
|
"awq": olmo_loader.awq,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
olmo_model.OLMoForCausalLM,
|
|
supports_awq=True,
|
|
supports_per_tensor=True,
|
|
),
|
|
),
|
|
"olmo2": Model(
|
|
name="olmo2",
|
|
model=olmo2_model.OLMo2ForCausalLM,
|
|
config=olmo2_model.OLMo2Config,
|
|
source={
|
|
"huggingface-torch": olmo2_loader.huggingface,
|
|
"huggingface-safetensor": olmo2_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
olmo2_model.OLMo2ForCausalLM,
|
|
supports_per_tensor=True,
|
|
),
|
|
),
|
|
"nemotron": Model(
|
|
name="nemotron",
|
|
model=nemotron_model.NemotronForCausalLM,
|
|
config=nemotron_model.NemotronConfig,
|
|
source={
|
|
"huggingface-torch": nemotron_loader.huggingface,
|
|
"huggingface-safetensor": nemotron_loader.huggingface,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
nemotron_model.NemotronForCausalLM,
|
|
supports_awq=True,
|
|
supports_per_tensor=True,
|
|
),
|
|
),
|
|
"bert-bge": Model(
|
|
name="bert-bge",
|
|
model=bert_model.BertModel,
|
|
config=bert_model.BertConfig,
|
|
source={
|
|
"huggingface-torch": bert_loader.huggingface_bge,
|
|
"huggingface-safetensor": bert_loader.huggingface_bge,
|
|
},
|
|
quantize=make_quantization_functions(
|
|
bert_model.BertModel,
|
|
),
|
|
model_task="embedding",
|
|
embedding_metadata=EmbeddingMetadata(
|
|
model_type="encoder",
|
|
pooling_strategy="cls",
|
|
normalize=True,
|
|
),
|
|
),
|
|
}
|