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...
"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...
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...
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):]}"...
CUDA_VISIBLE_DEVICES=0 \swift infer \--ckpt_dir "output/llama3-8b-instruct/vx-xxx/checkpoint-xxx" \--load_dataset_config true \--use_flash_attn true \--max_new_tokens 2048 \--temperature 0.1 \--top_p 0.7 \--repetition_penalty 1. \--do_sample true \--merge_lora false \ ...
do_sample: false max_new_tokens: 512 实际使用py-spy查看传入的参数的时候,显示do_sample=true, 没有max_new_tokens,而max_len是我的cut off len.实际上我是想限制生成长度。 根据transformers源代码 https://github.com/huggingface/transformers/blob/8bd2b1e8c23234cd607ca8d63f53c1edfea27462/src/trans...
{"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}generate_ids=model.generate(**...
( 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, ) ...
\ --load_dataset_config true --show_dataset_sample 10 \ --do_sample false # merg...
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...