参数介绍GenerationConfig 以text_generation为例。 huggingface GenerationConfig参数介绍 控制输出长度的: max_length,默认是20。表示最大的 输入+输出 的长度。效果会被max_new_tokens覆盖。 max_new_tokens,最大的输出的长度。 min_length,默认是0。表示最小的 输入+输出 的长度。效果会被min_new_tokens覆盖。
model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and...
downloading https://hf-mirror.com/internlm/internlm2-chat-7b/resolve/4275caa205dbb8ff83930e2c1ce6bc62ec49329c/generation_config.json to /home/hello/.cache/huggingface/hub/tmptspu0hwt downloading https://hf-mirror.com/internlm/internlm2-chat-7b/resolve/4275caa205dbb8ff83930e2c1ce6bc62ec49...
Generation config I know it has just been added so it is normal! But the following are missing (and are pretty intuitive w.r.t our other objects such as configs, processors etc): GenerationConfig.from_pretrained("openai/whisper-tiny.en" ...
ModelForCausalLM.from_pretrained(model_name,trust_remote_code=True,device_map="sequential",torch_dtype=torch.bfloat16,max_memory=max_memory,attn_implementation="eager")model.generation_config=GenerationConfig.from_pretrained(model_name)model.generation_config.pad_token_id=model.generation_config.eos_...
Feature request 👋 The request is for a way to pass a GenerationConfig to a Seq2SeqTrainer (through Seq2SeqTrainingArguments). Motivation ATOW, Seq2SeqTrainer only supports a few arguments for generation: max_length / max_new_tokens, num_...
sampling_rate = model.generation_config.sample_rate Audio(speech_output[0].cpu().numpy(), rate=sampling_rate) 访问阅读原文试听或下载该音频文件。 重要说明 上例中运行次数较少。为了测量和后续对比的准确性,运行次数需要增加到至少 100。 增加nb_loops一个主要原因是,同一输入的多次运行所生成的语音长度差...
peft_config = LoraConfig(task_type=TaskType.SEQ_CLS,inference_mode=False,r=8,lora_alpha=32,lora_dropout=0.1, ) trainer = RewardTrainer(model=model,args=training_args,tokenizer=tokenizer,train_dataset=dataset,peft_config=peft_config, )
一个完整的transformer模型主要包含三部分:Config、Tokenizer、Model。 Config 用于配置模型的名称、最终输出的样式、隐藏层宽度和深度、激活函数的类别等。 示例: {"architectures":["BertForMaskedLM"],"attention_probs_dropout_prob":0.1,"gradient_checkpointing":false,"hidden_act":"gelu","hidden_dropout_prob...
trainer = RewardTrainer(model=model,args=training_args,tokenizer=tokenizer,train_dataset=dataset,peft_config=peft_config, ) trainer.train() RLHF微调(用于对齐) 在这一步中,我们将从第1步开始训练SFT模型,生成最大化奖励模型分数的输出。具体来说就是将使用奖励模型来调整监督模型的输出,使其产生类似人类的...