Optimization Gptq 集成

# GPTQ 集成指南 与 transformers、PEFT、vLLM 及其他框架的集成。 ## Transformers 集成 ### 自动检测 python from transformers import AutoModelForCausalLM # 自动检测并加载 GPTQ 模型 model = AutoModelForCausalLM.from_pretrained( "TheBloke/Llama-2-13B-GPTQ", device_map="auto" ) ### 手动加载 python from auto_gptq import AutoGPTQForCausalLM model = AutoGPTQForCausalLM.from_quantized( "TheBloke/Llama-2-13B-GPTQ", device="cuda:0", use_exllama=True ) ## QLoRA 微调 python from transformers import AutoModelForCausalLM, TrainingArguments from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model from trl import SFTTrainer # 加载 GPTQ 模型 model = AutoModelForCausalLM.from_pretrained( "TheBloke/Llama-2-70B-GPTQ", device_map="auto" ) # 准备训练 model = prepare_model_for_kbit_training(model) # LoRA 配置 lora_config = LoraConfig( r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) model = get_peft_model(model, lora_config) # 训练 (在单张 A100 上训练 70B 模型!) trainer = SFTTrainer( model=model, train_dataset=dataset, max_seq_length=2048, args=TrainingArguments( per_device_train_batch_size=1, gradient_accumulation_steps=16, learning_rate=2e-4, num_train_epochs=3, output_dir="./results" ) ) trainer.train() ## vLLM 集成 python from vllm import LLM, SamplingParams # 在 vLLM 中加载 GPTQ 模型 llm = LLM( model="TheBloke/Llama-2-70B-GPTQ", quantization="gptq", dtype="float16", gpu_memory_utilization=0.95 ) # 生成 sampling_params = SamplingParams( temperature=0.7, top_p=0.9, max_tokens=200 ) outputs = llm.generate(["Explain AI"], sampling_params) ## 文本生成推理 (TGI) bash # 支持 GPTQ 的 Docker docker run --gpus all -p 8080:80 -v $PWD/data:/data ghcr.io/huggingface/text-generation-inference:latest --model-id TheBloke/Llama-2-70B-GPTQ --quantize gptq ## LangChain 集成 python from langchain.llms import HuggingFacePipeline from transformers import AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-13B-GPTQ") model = AutoModelForCausalLM.from_pretrained( "TheBloke/Llama-2-13B-GPTQ", device_map="auto" ) pipe = pipeline("text-gener