eos_token_id=2,hidden_act="silu",hidden_size=4096,initializer_range=0.02,intermediate_size=11008,max_position_embeddings=32768,model_type="llama",num_attention_heads=32,num_hidden_layers=32,pad_token_id=0,rms_norm_eps=1e-06,tie_word_embeddings=False,torch_dtype="float16",transformers_...
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"...
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 AI代码解释 pad_id = self.tokenizer.convert_tokens_to_ids("<|eot_id|>") self.tokenizer.pad_token_id = pad_id ...
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"] + [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_ids"] + [tokenizer.pad_token_id] if...
num_attention_heads=16, num_key_value_heads=4, rope_scaling = None, hidden_act='silu', max_position_embeddings=128, initializer_range=0.02, rms_norm_eps=1e-06, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, pretraining_tp = 1, max...
response = tokenizer(f"{example['output']}<|eot_id|>", add_special_tokens=False) 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...
token:这里不要理解为令牌,它的正确解释应该是一组向量的id。就是常见的描述大模型上下文长度的单位。一个token代表什么?互联网上有很多错误的解释,比较常见说法是:一个英文单词为1个token,一个中文通常是2-3个token。上面的流程介绍一节,我已经解释了“分析预测”与“采样推理”如何交互。“推理采样”生成1个toke...
[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 ...
pad_token_id = ( 0 # unk. we want this to be different from the eos token ) tokenizer.padding_side = "left" 这段代码使用来自Transformers库的LlamaForCausalLM类加载预训练的Llama 模型。load_in_8bit=True参数使用8位量化加载模型,以减少内存使用并提高推理速度。 代码还使用LlamaTokenizer类为同一...