forced_eos_token_id (int, optional):达到最大长度max_length时,强制作为最后生成的token id。 remove_invalid_values (bool, optional):是否删除模型nan(not a number)和inf(正无穷)防止崩溃,但可能会减慢生成速度。 exponential_decay_length_penalty (tuple(int, float), optional):生成一定数量的token之后,施...
length_penalty (浮点数,可选,默认为 1.0): 用于基于束生成的指数惩罚。它作为序列长度的指数使用,进而用于除以序列的分数。因为分数是序列的对数似然(即负数),所以 length_penalty > 0.0 促进较长序列,而 length_penalty < 0.0 鼓励较短序列。 no_repeat_ngram_size (整数,可选,默认为 0): 如果设置大于 0...
The paramater for encoder_repetition_penalty. An exponential penalty on sequences that are not in the original input. 1.0 means no penalty. length_penalty (`float`, *optional*, defaults to 1.0): Exponential penalty to the length that is used with beam-based generation. It is applied as an...
device=inputs_tensor.device, length_penalty=generation_config.length_penalty, do_early_stopping=generation_config.early_stopping, num_beam_hyps_to_keep=generation_config.num_return_sequences, num_beam_groups=generation_config.num_beam_groups, max_length=generation_config.max_length, ) # 12. interl...
* `length_penalty`:控制生成文本长度的惩罚因子。较小的值将鼓励更长的文本,而较大的值将鼓励更短的文本。 * `early_stopping`:如果为True,则提前停止生成过程,当生成的文本与之前生成的文本相似时。 * `use_cache`:如果为True,则缓存输入ID的转换结果以提高生成速度。 这些参数可以根据需要进行调整,以获得最...
min_length=kwargs.get("min_length", 1), top_p=kwargs.get("top_p", 1.0), repetition_penalty=kwargs.get("repetition_penalty", 1.0), length_penalty=kwargs.get("length_penalty", 1.0), length_penalty=kwargs.get("length_penalty", 0.8), temperature=kwargs.get("temperature", 1.0), atten...
beam_output = model.generate( sample, ... logits_processor_list=logits_processor_list, early_stopping=True, num_return_sequences=k, ... do_sample=False, output_scores=True, return_dict_in_generate=True, length_penalty=0, ) Expected behavior ...
{ 'max_length': 512, 'max_new_tokens': None, 'num_beams': 1, 'do_sample': False, 'use_past': True, 'temperature': 1.0, 'top_k': 0, 'top_p': 1.0, 'repetition_penalty': 1.0, 'encoder_repetition_penalty': 1.0, 'renormalize_logits': False, 'pad_token_id': 2, 'bos_token...
[`~generation.GenerationMixin.contrastive_search`]if`penalty_alpha>0.`and`top_k>1`-*multinomial sampling*by calling[`~generation.GenerationMixin.sample`]if`num_beams=1`and`do_sample=True`-*beam-search decoding*by calling[`~generation.GenerationMixin.beam_search`]if`num_beams>1`and`do_sample...
prompt_length = encoded_prompts.size(1) gen = generate( model=model, prompt=encoded_prompts, max_new_tokens=max_new_tokens, im_end_id=im_end_id, decode_one_token=decode_one_token, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, ) gen_tokens ...