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Meta-Llama-3.1-8B-bnb-4bit 加载：

from huggingface_hub import InferenceClient from torch import nn from transformers import ( AutoModel, AutoProcessor, AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizer, PreTrainedTokenizerFast, BitsAndBytesConfig, ) from pathlib import Path import torch from PIL import Image import os # =============================== # CLIP (SigLIP) # # =============================== # model_id = "google/siglip-so400m-patch14-384" # CLIP_PATH = download_hg_model(model_id, "clip") # clip_processor = AutoProcessor.from_pretrained( # CLIP_PATH, # trust_remote_code=True # ) # clip_model = AutoModel.from_pretrained( # CLIP_PATH, # trust_remote_code=True # ) # clip_model = clip_model.vision_model # clip_model.eval() # clip_model.requires_grad_(False) # clip_model.to("cuda") # =============================== # LLM (LLaMA 3.1 4bit) # =============================== MODEL_PATH = "/data/lbg/models/textoon/ComfyUI/models/LLM/Meta-Llama-3.1-8B-bnb-4bit" tokenizer = AutoTokenizer.from_pretrained( MODEL_PATH, trust_remote_code=True, use_fast=True ) assert isinstance( tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast) ), f"Tokenizer is of type {type(tokenizer)}" # ⭐ 2️⃣ bitsandbytes 4bit 配置（关键） bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, ) # ⭐ 3️⃣ 正确加载 4bit 模型 text_model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, quantization_config=bnb_config, device_map="auto", trust_remote_code=True, ) text_model.eval()

pip install bitsandbytes