使用感受上,发现llama3确实比基于llama2开发的模型效果要好很多的,比如指令跟随性就要好很多,不用在提示词工程上耗用太多时间。但是,也有发现一个问题,就是发现llama3模型虽然可以输出正确的答案,但是推理时间却是先前的好几倍,debug发现每次llama3推理时输出根本停不下来,直到达到max_new_tokens限定的tokens数量才会停...
max_new_tokens=512, eos_token_id=tokenizer.encode('<|eot_id|>')[0] ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(respo...
Please, answer in pirate-speak."},]outputs = pipe( messages, max_new_tokens=256, do_sample=False,)assistant_response = outputs[]["generated_text"][-1]["content"]print(assistant_response)# Arrrr, me hearty! Yer lookin' fer a bit o' information about meself, eh? Alright then...
model=model, model_kwargs={"temperature": temperature,"top_p": top_p, "max_new_tokens": 1000} return llm (prompt) def chat_completion ( messages: List [Dict], model = DEFAULT_MODEL, temperature: float = 0.6, top_p: float = 0.9, ) -> str: history = ChatMessageHistory () for m...
max_new_tokens– 指模型可以在其输出中生成的最大令牌数。 top_p– 指模型在生成输出时可以保留的令牌的累积概率 温度– 指模型生成的输出的随机性。温度大于 0 或等于 1 会增加随机性级别,而温度为 0 将生成最有可能的标记。 LLM应该根据LLM的用例选择超参数并对其进行适当的测试。 Llama 系列等型号要求LLM...
=16, num_key_value_heads=4, rope_scaling = None, hidden_act='silu', max_position_embeddings=128, initializer_range=0.02, rms_norm_eps=1e-06, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, pretraining_tp = 1, max_new_tokens = 100...
\n<|im_end|>"output = llm(input, temperature=0.8, top_k=50,max_tokens=256, stop=["<|im_end|>"])print(output) 7. Llama3模型微调和微调后推理 我们使用swift来对模型进行微调, swift是魔搭社区官方提供的LLM&AIGC模型微调推理框架. 微调代码开源地址链接...
max_new_tokens=256 ) 1. 2. 3. 4. 5. 6. 得到如下结果: Once upon a time, in a beautiful garden, there lived a little rabbit named Peter Rabbit. Peter had a friend named Rosie. They loved to play together. They would run, jump, and laugh all day long. ...
"},]prompt=pipeline.tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)terminators=[tokenizer.eos_token_id,tokenizer.convert_tokens_to_ids("")]outputs=pipeline(prompt,max_new_tokens=256,eos_token_id=terminators,do_sample=True,temperature=0.6,top_p=0.9,)print(outputs[...
model_id="meta-llama/Meta-Llama-3.1-8B-Instruct"pipe=pipeline("text-generation",model=model_id,model_kwargs={"torch_dtype":torch.bfloat16},device="cuda",)messages=[{"role":"user","content":"Who are you? Please, answer in pirate-speak."},]outputs=pipe(messages,max_new_tokens=256,do...