llama_token* enc_input_buf =embd_inp.data();if(llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size,0,0))) { LOG_TEE("%s : failed to eval\n", __func__);return1; } llama_token decoder_start_token_id=llama_model_decoder_start_token(model);if(decoder_start_toke...
tokenizer.pad_token_id = 128004 tokenizer.padding_side = 'right' 在添加新token前,先检查tokenizer如何处理我们计划用作自定义token的文本字符串,以便进行后续比较。我们将添加用于表示LLM输出中思考(think)和回答(answer)部分的token,总共4个token。 tokenizer("<think></think><answer></answer") 输出结果: {...
tokenizer.pad_token = "<|finetune_right_pad_id|>" tokenizer.pad_token_id = 128004 tokenizer.padding_side = 'right' 1. 2. 3. 4. 5. 6. 7. 8. 在添加新token前,先检查tokenizer如何处理我们计划用作自定义token的文本字符串,以便进行后续比较。我们将添加用于表示LLM输出中思考(think)和回答(answ...
= 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to( ...
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') ...
defget_model():tokenizer=AutoTokenizer.from_pretrained(mode_name_or_path,trust_remote_code=True)tokenizer.pad_token=tokenizer.eos_token model=AutoModelForCausalLM.from_pretrained(mode_name_or_path,torch_dtype=torch.bfloat16).cuda()returntokenizer,model ...
# 假设 pad_token 就是 eos_token() # 从右边填充 Once upon a time ... # 从左边填充 Once upon a time ... 1. 2. 3. 4. 5. 3.3 模型实例化 接下来就是实例化模型,这里就不用从预训练模型加载 from_pretrained() 了,而是从配置加载 from_config(): # 模型 import torch from transformers im...
= AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, use_cache = False, device_map=device_map)model.config.pretraining_tp = 1# Load the tokenizertokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)tokenizer.pad_token = tokenizer.eos_token...
tokenizer.pad_token = tokenizer.eos_token 1. 2. 2)消息格式查看 AI检测代码解析 messages = [ {"role": "system", "content": "现在你要扮演皇帝身边的女人--甄嬛"}, {"role": "user", "content": '你好呀'}, {"role": "assistant", "content": "你好,我是甄嬛,你有什么事情要问我吗?"}...
tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "right" 下面是参数定义, # Activate 4-bit precision base model loading use_4bit = True # Compute dtype for 4-bit base models bnb_4bit_compute_dtype = "float16" # Quantization type (fp4 or nf4) ...