stream = client.generate_stream(prompt, **gen_kwargs) # yield each generated token for r in stream: # skip special tokens if r.token.special: continue # stop if we encounter a stop sequence if r.token.text in gen_kwargs["stop_sequences"]: break # yield the generated token print(r....
=1024, stop_sequences=["\nUser:", "<|endoftext|>"], temperature=temperature) #stop_sequences to not generate the user answer # 用于累积生成的文本 acc_text = "" #Streaming the tokens # 遍历生成的词元流 for idx, response in enumerate(stream): # 获取单个词元的文本 text_token = ...
我们按照GitHub’s [instructions来设置personal access token私人访问token来将访问次数提高为一小时5000次,这个时候需要提供heads(包含token),如下所示。 GITHUB_TOKEN = xxx # Copy your GitHub token here headers = {"Authorization": f"token {GITHUB_TOKEN}"} 私有token请不要共享,保护隐私。 现在设置好...
class HuggingFacePipeline(LLM):\n ...\n def _call(\n ...\n if stop is not None:\n # This is a bit hacky, but I can\'t figure out a better way to enforce\n # stop tokens when making calls to huggingface_hub.\n text = enforce_stop_tokens(text, stop)\n return text\n Run...
>>> output_ids = model.generate(tokenizer=tokenizer, max_new_tokens=4, stop_strings="a") 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...
best_of: Generate best_of sequences and return the one if the highest token logprobs, default to null. details: Whether or not to return details about the generation. Default value is false. return_full_text: Whether or not to return the full text or only the generated pa...
Also you would want to check if the output is an end-of-sentence token, in which case the transformer thinks the translation is done and you can stop the inference. I am by no means an expert on HuggingFace, but I'm fairly certain that they provide helper functions for constructing the...
The more a token is used within generation the more it is penalized to not be picked in successive generation passes. ResultsPerPrompt (Default: 1). Integer. The number of proposition you want to be returned. ReturnFullText (Default: True). Bool. If set to False, the ...
ifinput_idsinself.keywords:returnTruereturnFalsestop_words = ['}',' }','\n'] stop_ids = [tokenizer.encode(w)forwinstop_words] stop_ids.append(tokenizer.eos_token_id) stop_criteria = KeywordsStoppingCriteria(stop_ids) model.generate( text_inputs='some text:{', StoppingCriteria=stop_...
Avoid dummy token in PLD to optimize performance by @ofirzaf in #29445 Fix test failure on DeepSpeed by @muellerzr in #29444 Generate: torch.compile-ready generation config preparation by @gante in #29443 added the max_matching_ngram_size to GenerationConfig by @mosheber in #29131 ...