mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-20 14:22:08 +00:00
bab02ff2bc
* feat: add ruff and resolve issue * fix: update client exports and adjust after rebase * fix: adjust syntax to avoid circular import * fix: adjust client ruff settings * fix: lint and refactor import check and avoid model enum as global names * fix: improve fbgemm_gpu check and lints * fix: update lints * fix: prefer comparing model enum over str * fix: adjust lints and ignore specific rules * fix: avoid unneeded quantize check
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
def load_text_model(prefix, config, weights, name=None):
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if config.model_type == "llama":
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from text_generation_server.models.custom_modeling.flash_llama_modeling import (
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FlashLlamaForCausalLM,
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)
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return FlashLlamaForCausalLM(prefix, config, weights)
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elif config.model_type == "mistral":
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from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
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FlashMistralForCausalLM,
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)
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return FlashMistralForCausalLM(prefix, config, weights, name=name)
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elif config.model_type == "gemma":
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from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
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FlashGemmaForCausalLM,
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)
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return FlashGemmaForCausalLM(prefix, config, weights, causal=False)
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elif config.model_type == "paligemma":
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from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
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FlashGemmaForCausalLM,
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)
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return FlashGemmaForCausalLM(prefix, config, weights)
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else:
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raise RuntimeError(f"Unsupported model type {config.model_type}")
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def load_vision_model(prefix, config, weights):
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if config.model_type == "clip_vision_model":
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from text_generation_server.models.custom_modeling.clip import (
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CLIPVisionTransformer,
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)
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return CLIPVisionTransformer(
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prefix=f"{prefix}.vision_model", config=config, weights=weights
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)
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if config.model_type == "siglip_vision_model":
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from text_generation_server.models.custom_modeling.siglip import (
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SiglipVisionTransformer,
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)
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return SiglipVisionTransformer(
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prefix="vision_tower.vision_model", config=config, weights=weights
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)
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else:
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raise RuntimeError(f"Unsupported model type {config.model_type}")
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