然后,根据 config.rescale_every 参数的设置,对每个 RwkvBlock 中的注意力权重和前馈传播权重进行重新缩放。 如果模型处于训练状态,会将 block.attention.output.weight 和 block.feed_forward.value.weight 分别乘以 2 的 (block_id // self.config.rescale_every) 次方。这样做的目的是根据 config.rescale_every ...
转载:【peft】huggingface大模型加载多个LoRA并随时切换-CSDN博客 from peft import PeftModel from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig model_name = "decapoda-research/llama-7b-hf" tokenizer = LlamaTokenizer.from_pretrained(model_name) model = LlamaForCausalLM.from_pretraine...
config=config, train_dataset=train_dataset, tokenizer=tokenizer, )forepoch, batchintqdm(enumerate(ppo_trainer.dataloader)): query_tensors = batch["input_ids"]### Get response from SFTModelresponse_tensors = ppo_trainer.generate(query_tensors, **generation_kwargs) batch["response"] = [tokeniz...
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,)trainer.train()
Hugging Face 文档: 无条件图像生成 (Unconditional Image-Generation),包含了有关如何使用官方训练示例脚本训练扩散模型的一些示例,包括演示如何创建自己的数据集的代码: https://hf.co/docs/diffusers/training/unconditional_training AI Coffee Break video on Diffusion Models: ...
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...
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...
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" ...
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...