[ { "instruction": "user instruction (required)", "input": "user input (optional)", "output": "model response (required)", "system": "system prompt (optional)", "history": [ ["user instruction in the first round (optional)", "model response in the first round (optional)"], ["us...
在Alpaca-LoRA 项目中,作者提到,为了廉价高效地进行微调,他们使用了 Hugging Face 的 PEFT。PEFT 是一个库(LoRA 是其支持的技术之一,除此之外还有Prefix Tuning、P-Tuning、Prompt Tuning),可以让你使用各种基于 Transformer 结构的语言模型进行高效微调。下面安装PEFT。 git clone https://github.com/huggingface/pef...
PEFT 是一个库(LoRA 是其支持的技术之一,除此之外还有Prefix Tuning、P-Tuning、Prompt Tuning),可以让你使用各种基于 Transformer 结构的语言模型进行高效微调。下面安装PEFT。 git clone https://github.com/huggingface/peft.git cd peft/ git checkout e536616 pip install . 安装bitsandbytes。 git clone ...
考虑到alpaca数据集大部分都是["instruction", "input", "output"]型格式,因此我们为key值["prompt", "query", "response"]设置了默认值。因此上面格式--map-keys参数可简略为'{"system": "system","history": "history"}' 若数据集中无system与history列,则--map-keys可省略。 【--prompt-type】 用于...
考虑到alpaca数据集大部分都是["instruction", "input", "output"]型格式,因此我们为key值["prompt", "query", "response"]设置了默认值。因此上面格式--map-keys参数可简略为'{"system": "system","history": "history"}' 若数据集中无system与history列,则--map-keys可省略。
prompt = output.prompt # 获取原始的输入提示 generated_text = output.outputs[0].text # 从输出对象中获取生成的文本 print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") 运行脚本: python test_vllm.py ---CUT--- 换新: 运行成功。 ---CUT--- 提示要装cuda版本的pytorch 用conda...
device_map='auto')local_llm=HuggingFacePipeline(pipeline=pipeline)template="""Question: {question}Answer: Let's think step by step."""prompt=PromptTemplate(template=template,input_variables=["question"])llm_chain=LLMChain(prompt=prompt,llm=local_llm)llm_chain.run('What is the capital of India...
G Prompt reply within 12 hours H Various avaliable material for selection I Provide the best quality and five star service for client J Strict quality control system to guarantee the quality K Professional desiger to customize caps for customer L Fast sample lead...
help="Whether use the system prompt and template of Chinese-Alpaca-2 when constructing the instructions.") parser.add_argument('--e', action='store_true', help="Evaluate on LongBench-E") parser.add_argument('--use_flash_attention_2', action='store_true', help="Use flash attention to...
I call it prompt-repeater model, and other developers had also been reporting this behavior in the repo issues. Its ability to answer questions only gets better with more carefully designed prompts. This behavior is not what we want in a production system, as we cannot guarantee prompt ...