safetensors model.save_pretrained("dir_path") bin torch.save("file_path.bin") 写这个的原因是发现使用hf的transformers库自带的save_pretrained时不会保留head 对比如下 # hf >>> model = AutoModel.from_pretrained("test") >>> model GPT2Model( (wte): Embedding(50257, 1024) (wpe): Embedding(1...
使用model.save_pretrained('PATH') 将模型保存到指定路径。 使用MODEL_NAME.from_pretrained('PATH') 来加载模型。 二、展示效果文本分类 from transformers import pipeline, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") model = pipeline("text-classification", model="distilbert...
原来embedding weight 不会变# 使用新的嵌入向量初始化新词汇的嵌入向量a = model.get_input_embeddings()print(a)# Embedding(30524, 768)tok = tokenizer.convert_tokens_to_ids(["newword"])print(tok)# [30522]# 保存微调后的模型和tokenizer(重要)model.save_pretrained("./gaibian") tokenizer.save_pre...
we could add padding if the model supported it instead of this drop, you can# customize this part to your needs from deep_hub.total_length = (total_length // block_size) * block_size# Split by chunks of max_len.result = {
model=model, args=training_args, train_dataset=lm_datasets["train"], eval_dataset=lm_datasets["validation"], ) trainer.train() 训练完成后,评估以如下方式进行: import math eval_results = trainer.evaluate() print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}") ...
Saving/Loading Model with Pipe ❌ (Failed) I tried to save the model withpipe.save_pretrained("./local_model_directory")and then load the model in the second run with `pipe("object-detection", model="./local_model_directory"). This throws an error and doesn't work at all. ...
本篇文章将分享如何通过 Docker 来在本地快速运行 Hugging Face 上的有趣模型。用比原项目更少的代码...
model = AutoModelForCausalLM.from_pretrained(model_name) # First, load the base model model.resize_token_embeddings(len(tokenizer)) # Then, resize the token embeddings model = PeftModel.from_pretrained(model, save_path) # <--- This works. You can now use model for ev...
trainer.save_model("./my_model") 奖励模式训练 RLHF训练策略用于确保LLM与人类偏好保持一致并产生更好的输出。所以奖励模型被训练为输出(提示、响应)对的分数。这可以建模为一个简单的分类任务。奖励模型使用由人类注释专家标记的偏好数据作为输入。下面是训练奖励模型的代码。
使用的时候,非常简单。huggingface的transformers框架主要有三个类model类、configuration类、tokenizer类,这三个类,所有相关的类都衍生自这三个类,他们都有from_pretained()方法和save_pretrained()方法。 from_pretrained方法的第一个参数都是pretrained_model_name_or_path,这个参数设置为我们下载的文件目录即可。