创建提示 为了执行指令微调，我们必须将每个数据示例转换为指令，并将其主要部分概述如下:def format_instruction(sample): return f"""### Instruction:Use the Task below and the Input given to write the Response, which is a programming code that can solve the following Task:### Task:{sample['instr...
generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.5) # Print the result print(f"Prompt:\n{prompt}\n") print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}") ...
# Run the model to infere an output outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.5) # Print the result print(f"Prompt:\n{prompt}\n") print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(),...
split="train")sample = dataset[randrange(len(dataset))]prompt = f"""### Instruction:Use the Input below to create an instruction, which could have been used to generate the input using an LLM. ### Input:{sample['response']}#
generate_input = {"input_ids":input_ids,"max_new_tokens":512,"do_sample":True,"top_k":50,"top_p":0.95,"temperature":0.3,"repetition_penalty":1.3,"eos_token_id":tokenizer.eos_token_id,"bos_token_id":tokenizer.bos_token_id,"pad_token_id":tokenizer.pad_token_id ...
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.5) # Print the result print(f"Prompt:\n\n") print(f"Generated instruction:\n") 结果如下: Prompt: ### Instruction: Use the Task below and the Input given to write the Response...
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.5) # Print the result print(f"Prompt:\n{prompt}\n") print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(pr...
input_ids.to('cuda') generate_input = { "input_ids":input_ids, "max_new_tokens":512, "do_sample":True, "top_k":50, "top_p":0.95, "temperature":0.3, "repetition_penalty":1.3, "eos_token_id":tokenizer.eos_token_id, "bos_token_id":tokenizer.bos_token_id, "pad_token_id":...
generate_input = { "input_ids": input_ids, #输入的token "max_new_tokens": 512, #最大生成的token数量 "do_sample": True, #是否采样 "top_k": 50, #采样的top_k "top_p": 0.95, #采样的top_p "temperature": 0.3, #采样的temperature "repetition_penalty": 1.3, #重复惩罚 "eos_token_...
generate_input = { "input_ids":input_ids, "max_new_tokens":512, "do_sample":True, "top_k":50, "top_p":0.95, "temperature":0.3, "repetition_penalty":1.3, "eos_token_id":tokenizer.eos_token_id, "bos_token_id":tokenizer.bos_token_id, ...