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53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
from typing import (
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Literal,
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Self,
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)
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from pydantic import Field
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from typing_extensions import Any
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from invokeai.backend.model_manager.configs.base import Config_Base, Diffusers_Config_Base
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from invokeai.backend.model_manager.configs.identification_utils import (
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NotAMatchError,
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common_config_paths,
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get_class_name_from_config_dict_or_raise,
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raise_for_override_fields,
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raise_if_not_dir,
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)
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from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
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from invokeai.backend.model_manager.taxonomy import (
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BaseModelType,
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ModelType,
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)
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class TextLLM_Diffusers_Config(Diffusers_Config_Base, Config_Base):
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"""Model config for text-only causal language models (e.g. Llama, Phi, Qwen, Mistral)."""
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type: Literal[ModelType.TextLLM] = Field(default=ModelType.TextLLM)
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base: Literal[BaseModelType.Any] = Field(default=BaseModelType.Any)
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cpu_only: bool | None = Field(default=None, description="Whether this model should run on CPU only")
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@classmethod
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def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self:
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raise_if_not_dir(mod)
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raise_for_override_fields(cls, override_fields)
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# Check that the model's architecture is a causal language model.
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# This covers LlamaForCausalLM, PhiForCausalLM, Phi3ForCausalLM, Qwen2ForCausalLM,
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# MistralForCausalLM, GemmaForCausalLM, GPTNeoXForCausalLM, etc.
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class_name = get_class_name_from_config_dict_or_raise(common_config_paths(mod.path))
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if not class_name.endswith("ForCausalLM"):
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raise NotAMatchError(f"model architecture '{class_name}' is not a causal language model")
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# Verify tokenizer files exist to avoid runtime failures
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tokenizer_files = {"tokenizer.json", "tokenizer.model", "tokenizer_config.json"}
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if not any((mod.path / f).exists() for f in tokenizer_files):
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raise NotAMatchError(
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f"no tokenizer files found in '{mod.path}' "
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f"(expected at least one of: {', '.join(sorted(tokenizer_files))})"
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)
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return cls(**override_fields)
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