input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id] attention_mask = instruction["attention_mask"] + response["attention_mask"] + [1] # 因为eos token也需要关注,所以补充为1 labels = [-100] * len(instruction["input_ids"]) + response["input_ids"...
out_logprobs.append(probs) 1. 我们可以直接把结束符设置为self.tokenizer.pad_token = "<|eot_id|>" 2. 也可以直接查看stop_tokens的id: 代码语言:txt 复制 pad_id = self.tokenizer.convert_tokens_to_ids("<|eot_id|>") self.tokenizer.pad_token_id = pad_id...
输入参数:只是一个inputs_id 输出参数:hidden_states, attention_mask, position_ids class PipeEmbedding(nn.Module): def __init__(self, config: LlamaConfig) -> None: super().__init__() self.padding_idx = config.pad_token_id self.embed_tokens = nn.Embedding( config.vocab_size, config.hid...
attention_mask=attention_mask, pad_token_id=tokenizer.eos_token_id,) generated_ids= [output_ids[len(input_ids):]forinput_ids, output_idsinzip(model_input.input_ids, generated_ids)] response= tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]print(f'{response} \n') 运行...
{"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(**...
decoder_start_token_id=llama_token_bos(model); } embd_inp.clear(); embd_inp.push_back(decoder_start_token_id); } (3) 分析预测 分析预测部分的核心代码如下,我将处理关注力和session的逻辑删除,仅保留推理部分的逻辑。 //predictif(!embd.empty()) {//Note: (n_ctx - 4) here is to match ...
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) ...
{"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(**...
tokenizer.pad_token = tokenizer.eos_token 接着,设置pyreft配置,然后使用pyreft.get_reft_model()方法准备好模型。 # get reft model reft_config = pyreft.ReftConfig(representations={ "layer": 8, "component": "block_output", "low_rank_dimension": 4, ...
[00:15<00:00, 7.79s/it] Human:写一个快速排序算法 The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. Setting `pad_token_id` to `eos_token_id`:128001 for ...