在temperature 方法里面,会依次把下面的函数都执行一遍,我暂时还没琢磨清楚为什么。 llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); llama_sample_top_p...
4. sample_top_p函数:这个函数用于从给定的概率分布中采样一个token,采样的方式是先对概率进行排序,然后计算累积概率,然后选择累积概率小于p的部分,最后在这部分中随机选择一个token。 下面将具体讲解每一个部分的代码: import json import os import sys import time from pathlib import Path from typing import ...
top_p: float = 0.9,) -> str:llm = Replicate (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,) -...
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...
payload={“inputs”:str,(optional)"parameters":{"max_new_tokens":int,"top_p":float,"temperature":float}} The following are some sample example prompts and the text generated by the model. All outputs are generated with inference parameters{"max_new_tokens":64, "top_p":0.9, ...
outputs = model.generate(input_ids=input_ids,max_new_tokens=100,do_sample=True,top_p=0.9,temperature=0.5) #Printthe resultprint(f"Prompt:\n{prompt}\n")print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}...
可能性最小的 token 会从列表中「剪切」(基于 top_p),然后从剩余候选者中随机(温度参数 temperature)选择一个 token。换句话说:top_p 控制生成中词汇的广度,温度控制词汇的随机性,温度参数 temperature 为 0 会产生几乎确定的结果。 def print_tuned_completion (temperature: float, top_p: float): response ...
Llama中文社区,最好的中文Llama大模型,完全开源可商用. Contribute to pengjinning/Llama2-Chinese development by creating an account on GitHub.
streamer=TextIteratorStreamer(self.tokenizer,skip_prompt=True,skip_special_tokens=True)generate_kwargs=dict(model_inputs,streamer=streamer,max_new_tokens=max_generated_tokens,do_sample=True,top_p=top_p,temperature=float(temperature),top_k=top_k,eos_token_id=self.tokenizer.eos_token_id)t=Thread...
Llama3 中文仓库(聚合资料:各种网友及厂商微调、魔改版本有趣权重 & 训练、推理、部署教程视频 & 文档) - AICodeHunt/llama3-Chinese-chat