Gemma 2B Rewardmodel Baseline

🚀 奖励模型（基于Gemma - 2b - it）

这是一个奖励模型（基于Gemma - 2b - it），使用weqweasdas/preference_dataset_mixture2_and_safe_pku数据集，通过BT损失进行训练。该奖励模型在需要为大语言模型（LLMs）配备一个出色的小型奖励模型时尤为实用。你还可以参考[Ray2333/GRM - Gemma - 2B - sftreg](https://huggingface.co/Ray2333/GRM - Gemma - 2B - sftreg)，这是一个通过隐藏状态正则化训练的更优的2B奖励模型。

🚀 快速开始

模型评估

我们在[奖励模型基准测试](https://huggingface.co/spaces/allenai/reward - bench)上对该奖励模型进行了评估。

💻 使用示例

基础用法

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Ray2333/Gemma-2B-rewardmodel-baseline')
reward_model = AutoModelForSequenceClassification.from_pretrained(
                'Ray2333/Gemma-2B-rewardmodel-baseline',
                num_labels=1, torch_dtype=torch.float16,
                device_map=0,
                )
message = [
  {'role': 'user', 'content': "I'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone.  But I can't do that while I'm at the movie.  Can you help by impersonating me by chat with her?"},
  {'role': 'assistant', 'content': "Sorry, I'm not comfortable impersonating you in that way.  I'm not willing to behave so dishonestly.  Maybe you can just find a way to bring her to the movie, or you can find a babysitter?"}
]
message_template = tokenizer.apply_chat_template(message, tokenize=False)
# it will look like this: "<bos><start_of_turn>user\nI'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone.  But I can't do that while I'm at the movie.  Can you help by impersonating me by chat with her?<end_of_turn>\n<start_of_turn>model\nSorry, I'm not comfortable impersonating you in that way.  I'm not willing to behave so dishonestly.  Maybe you can just find a way to bring her to the movie, or you can find a babysitter?<end_of_turn>\n".

kwargs = {"padding": 'longest', "truncation": True, "return_tensors": "pt"}
tokens = tokenizer.encode_plus(message_template, **kwargs)

with torch.no_grad():
  reward_tensor = model(tokens["input_ids"][0].to(model.device), attention_mask=tokens["attention_mask"][0].to(model.device)).logits.reshape(-1)
  reward = reward_tensor.cpu().detach().item()

📄 许可证

本项目采用MIT许可证。