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。 early_stopping,默认是Fal...
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...
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" ...
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...
from transformersimportAutoTokenizer,AutoModelForCausalLM,GenerationConfig model_name="deepseek-ai/DeepSeek-V2"tokenizer=AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)#`max_memory`should besetbased on your devices max_memory={i:"75GB"foriinrange(8)}#`device_map`cannot besetto`...
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_...
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: 代码语言:javascript 复制 {"architectures":["BertForMaskedLM"],"attention_probs_dropout_prob":0.1,"gradient_checkpointing":false,"hidden_act":"gel...
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...
deepspeed--num_gpus=8 scripts/run_seq2seq_deepspeed.py --model_id google/flan-t5-xxl --dataset_path data --epochs 3 --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --generation_max_length 129 --lr 1e-4 --deepspeed configs/ds_flan_t5_z3_config_bf16.json ...
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...