pad_id = self.tokenizer.pad_id #填充字,在tokenizer中定义的填充字 # 生成一个shape 为(提示token的组数,total_len) 初始字符为pad_id的tokens tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda") ...# 接着将prompt_tokens填充至tokens prev_pos = 0 #初始位置为0...
tokenizer = AutoTokenizer.from_pretrained('/root/autodl-tmp/LLM-Research/Meta-Llama-3-8B-Instruct', use_fast=False, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token, tokenizer.pad_token_id, tokenizer.eos_token_id 此处输出的pad_token、pad_token_id和eos_to...
eos_idx = toks.index(stop_token) toks = toks[:eos_idx] probs = probs[:eos_idx] if logprobs else None except ValueError: pass out_tokens.append(toks) out_logprobs.append(probs) 1. 我们可以直接把结束符设置为self.tokenizer.pad_token = "<|eot_id|>" ...
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咱们也是要关注的所以 补充为1 labels = [-100] * len(instruction["input_ids"]) + response["input...
tokenizer.pad_id tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda") for k, t in enumerate(prompt_tokens): tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda") if logprobs: token_logprobs = torch.zeros_like(tokens, dtype=torch...
model_inputs = tokenizer([input_ids], return_tensors="pt").to('cuda') # 使用模型生成回复 generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512) # 从生成的ID中提取回复部分 generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_...
self.pad_id = self.processor.pad_id() tokennizer.encoder tokenizer.encode(prompt, bos=True, eos=False, device=fabric.device) def encode( self, string: str, bos: bool = True, eos: bool = False, max_length: int = -1, pad: bool = False, ...
[226291]: llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,122753] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 May 14 12:08:40 wbs...
Hello, all, as I know llama3 tokenizer is based on byte level BPE, But I can not find the relationship between the token_id and (0-255) byte map. For example, with character "Ä" , the utf-8 encode is b'\xc3\x84' = [195,132] . With llama3 tokenizer, "Ä" is encode ...
AutoTokenizer 几乎所有的NLP任务都以tokenizer开始,用它来加载模型对应的分词器。 AutoModel 真正来加载模型实例的是AutoModel,不同任务使用的AutoModel也不同,针对大语言模型一般使用AutoModelForCausalLM。 模型量化 量化技术专注于用较少的信息表示数据,同时尽量不损失太多准确性。 Transformers支持三种量化方法:AWQ、...