encode(stop_token)[0] _ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, do_sample=True, temperature=0.1, repetition_penalty=1.2, top_p=0.9, eos_token_id=stop_token_id) 👍 6 Sign up for free to join this conversation on GitHub. Already have an account? Sign in...
Base class from which `.generate()` streamers should inherit. """defput(self, value):"""Function that is called by `.generate()` to push new tokens"""# 抛出未实现错误,子类需要实现该方法raiseNotImplementedError()defend(self):"""Function that is called by `.generate()` to signal the en...
>>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) 'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.' MBart 以下M...
batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "\nUSER: What's the content of the image?\nASSISTANT: The image features a stop sign on a street corner" LXMERT 原始文本:huggingface.co/docs/transformers/v4.37.2/en/model_doc/lxmert 概述 LXMERT ...
return_token_timestamps:Optional[bool] =None, return_segments:bool=False, return_dict_in_generate:Optional[bool] =None, **kwargs,defgenerate_with_fallback(self, segment_input, decoder_input_ids, cur_bsz, batch_idx_map, seek, num_segment_frames, ...
model.generate()can take astop_stringsargument to use custom stop tokens for generation, but a tokenizer object needs to be passed as well. model.generate(..., stop_strings=["<stop token>"], tokenizer=tokenizer) If we addstop_stringstogeneration_config.json, which can be loaded correctlyco...
该机制基于这样一个事实,即基于 RNN 的模型(如 GRUs 或 LSTMs)在诸如token-id之类的任务上存在信息瓶颈,并以递归方式处理它(编码器)。然后,处理过的中间表示被馈送到另一个递归单元(解码器)以提取结果。这种雪崩般的信息就像一个滚动的球,消耗了所有的信息,而将其滚动出来对于解码器部分来说是困难的,因为解码...
token_type_ids(torch.LongTensor,形状为(batch_size, sequence_length),optional) — 段标记索引,指示输入的第一部分和第二部分。索引选择在[0, 1]之间: 0 对应于句子 A标记, 1 对应于句子 B标记。 什么是标记类型 ID? position_ids(torch.LongTensor,形状为(batch_size, sequence_length),optional) — ...
(device="cuda:1") generated_ids = llama_3_model.generate(llama_3_model_input.input_ids, max_new_tokens=4096) HF_generated_ids = HF_llama_3_model.generate(HF_llama_3_model_input.input_ids, max_new_tokens=4096) print(len(generated_ids[0]), len(HF_generated_ids[0])) generated_ids...
filtered_tokens = [token for token in tokens if token not in stop_words] return filtered_tokens##创建词汇表def create_vocabulary(tokens): vocabulary = set(tokens) return vocabulary##生成输入样本def generate_input_samples(texts, max_sequence_length): ...