最后,QLoRa是将量化应用于LoRa方法,允许4位正常量化,nf4,这是一种针对正态分布权重进行优化的类型;双重量化以减少内存占用,并优化NVIDIA统一内存。这些是优化内存使用的技术,以实现“轻量化”和更经济的训练。 在我的实验中使用QLoRa需要指定BitsAndBytes配置,下载4位量化的预训练模型,并定义一个LoraConfig。最后,...
model = model.merge_and_unload() 最后一个导入是SFTTrainer。SFTTrainer是transformer Trainer类的子类。Trainer是一个功模型训练的泛化API。SFTTrainer在此基础上增加了对参数微调的支持。有监督的微调步骤是训练因果语言模型(如Llama)用于下游任务(如指令遵循)的关键步骤。 SFTTrainer支持PEFT,因此我们将与LoRA一起使...
then I reload the base 16 bit model, mount the adapter, merge_and_unload, and save the merged model: del model del trainer model = AutoModelForCausalLM.from_pretrained( args.model_name, trust_remote_code=True, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch...
# Save only adapaterslora_model.save_pretrained(...)# Save merged modelmerged_model = lora_model.merge_and_unload() merged_model.save_pretrained(...)量化 谈到LoRA，我就还需要说一下量化。这两种技术在论文QLORA得到了高效的融合，并且已经通过bitsandbytes、peft和accelerayte整合到了Hugging Face 的...
As the title said, I want to merge my PEFT LoRA adapter model (ArcturusAI/Crystalline-1.1B-v23.12-tagger) that I trained before with the base model (TinyLlama/TinyLlama-1.1B-Chat-v0.6) and make a fully new model. And I got this code from ChatGPT: ...
peft import AutoPeftModelForCausalLMmodel = AutoPeftModelForCausalLM.from_pretrained( args.output_dir, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map=device_map, )# Merge LoRA and base modelmerged_model = model.merge_and_unload()# Save t...
I am trying to .merge_and_unload() a Llama 2 peft model to use it for inference in Databricks. Here is my code for training the model; I do add a pad token which I think is the cause of the error. target_modules = ['q_proj','k_proj','v_p...
merge_and_unload() model.save_pretrained( args.sm_model_dir, safe_serialization=True, max_shard_size="2GB" )Combining the LoRA adapter and base model into a single model artifact after fine-tuning has advantages and disadvantages. The combined model is self-contained and ...
Once the adapter is trained, it is merged with the original model before persisting the weights. Custom Model Import does not support LoRA adapters at the moment. model=model.merge_and_unload()model.save_pretrained(sagemaker_save_dir,safe_serialization=True,max_shard_size="2GB...
merged_model = model.merge_and_unload() merged_model.save_pretrained('lora') tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.save_pretrained('lora') In principle, I am loading the original model with the merged weights, finetune that on new data likewise with PEFT and LoRA and...