"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 } generate_ids = model.generate(**generate_input) text = tokenizer.decod...
num_beams: 每一时间步选择num_beams个词,并从中最终选择出概率最高的序列 Beam-search:do_sample = False, num_beams>1 Multinomial sampling(多项式采样): 每一个时间步,根据概率分布随机采样字(每个概率>0的字都有被选中的机会)。do_sample = True, num_beams = 1 Beam-search multinomial sampling:结合...
eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) 3.4 数据集部分放到后面一起说明 4. 原始模型直接推理 在进行后续的环节之前,我们先使用推理模式,先验证一下LLaMA-Factory的推理部分是否正常。LLaMA-Factory 带了基于gradio开发...
# 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(),...
do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=400, ) for seq in sequences: print (f"{seq ['generated_text']}") 步骤4:运行 Llama 现在,这个脚本已经可以运行了。保存脚本,回到 Conda 环境,输入 ...
do_sample=True, top_k=40, top_p=0.95, temperature=0.8 ) generated_text = tokenizer.decode( outputs[0], skip_special_tokens=True ) # print(outputs) print(generated_text) inference(model, tokenizer) ''' Once upon a time, Hostย crimeine /\ könnenlinewidth measurementresol perfectly Tay...
\n', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200,)for seq in sequences: print(f"Result: {seq['generated_text']}")Result: I liked "Breaking Bad" and "Band of Brothers". Do you have any recommendations...
max_new_tokens=200, do_sample=True, top_p=0.9, temperature=0.1,)output = output[].to("cpu")print(tokenizer.decode(output))使用 TGI 和推理终端 TGI 是 Hugging Face 开发的生产级推理容器，可用于轻松部署大语言模型。它包含连续批处理、流式输出、基于张量并行的多 GPU 快速推理以及生产...
( **inputs, pad_token_id=tokenizer.eos_token_id, max_new_tokens=max_new_tokens, do_sample=True, top_k=40, top_p=0.95, temperature=0.8 ) generated_text = tokenizer.decode( outputs[0], skip_special_tokens=True ) # print(outputs) print(generated_text) inference(model, tokenizer) '''...
( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) ...