output_texts = model.generate( input_ids=input_ids, attention_mask=attention_mask, pad_token_id= tokenizer.eos_token_id, eos_token_id= tokenizer.eos_token_id, max_new_tokens=500, do_sample=False, top_k=30, top_p=0.85, temperature=0.3, repetition_penalty=1.2) ...
模型推理时验证model.generate()功能报错。 Traceback (most recent call last): File "predict dx.py". line 26, in <module> result = model.generate(input_ids=inputs["input_ids"], max_length=model.config.seq_length) File "/home/wizardcoder/1_wiz ardcoder-mindformers7mindformers/generation/...
output=model.generate(tokenizer.encode(txt, return_tensors="pt"), max_new_tokens=5, num_return_sequences=1,return_dict_in_generate=True,output_scores=True,num_beams=1) 原文本编码后input_ids如下: tokenizer.encode(txt, return_tensors="pt") tensor([[ 32, 5882, 5882, 6378, 318, 257, ...
modelpath=r'/home/recall/models/QwenQwen-14B-Chat-Int8' tokenizer = AutoTokenizer.from_pretrained(modelpath, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( modelpath, device_map="auto", trust_remote_code=True ).eval() def invoke4(model,tokenizer,input): input_ids =...
outputs = self.model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], max_new_tokens=10, eos_token_id=3 ) outputs = self.tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True) ...
chat_history_ids = model.generate(bot_input_ids, do_sample=True, max_length=2000, top_k=50, top_p=0.95, pad_token_id=tokenizer.eos_token_id) The last step is to decode and print the response: Preparation for interactive conversation:After response generation, the last step...
def generate(self, input_ids=None, **kwargs): args = get_args() if parallel_state.get_data_parallel_world_size() > 1: raise ValueError("In this inference mode data parallel is forbidden.") super(MegatronModuleForCausalLM, self).generate(input_ids=input_ids, **kwargs) #...
bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)...
>>> outputs = model.generate(**inputs, do_sample=True, temperature=0.7, top_p=0.8, repetition_penalty=1.02, max_new_tokens=256) >>> response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) ...
The text was tokenized to form an input vector, which was the concatenation of “input IDs”, “attention mask”, and “token type IDs”. The input IDs were the numerical representations of words building the text; the attention mask was used to batch texts together; and token type IDs ...