---
title: Gemma-2B-rewardmodel-baseline
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Gemma-2B-rewardmodel-baseline"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/Gemma-2B-rewardmodel-baseline.md"
repository: AI-ModelScope/Gemma-2B-rewardmodel-baseline
last_updated: 2025-02-05
license: mit
pipeline_tag: text-classification
tasks:
  - text-classification
model_type:
  - gemma
architectures:
  - GemmaForSequenceClassification
parameters: 2.5B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
downloads: 1062
stars: 0
---

# Gemma-2B-rewardmodel-baseline

> Gemma-2B-rewardmodel-baseline - AI-ModelScope 在 ModelScope 开源的模型。Introduction This is a breward model (based on Gemma-2b-it) trained with BT loss using the weqweasdas/preferencedatasetmixture2andsafepku dataset.

AI-ModelScope/Gemma-2B-rewardmodel-baseline 是 ModelScope 魔搭社区上的 2.5B 参数text-classification模型，采用 mit 许可。

- **Repository**: AI-ModelScope/Gemma-2B-rewardmodel-baseline
- **License**: mit
- **Tasks**: text-classification
- **Parameters**: 2.5B
- **Downloads**: 1062
- **Stars**: 0
- **Last updated**: 2025-02-05

Source: https://www.modelscope.cn/models/AI-ModelScope/Gemma-2B-rewardmodel-baseline

---

# Introduction
This is a breward model (based on Gemma-2b-it) trained with BT loss using the [weqweasdas/preference_dataset_mixture2_and_safe_pku](https://huggingface.co/datasets/weqweasdas/preference_dataset_mixture2_and_safe_pku) dataset. 

This reward model is especially useful if you need a good small reward model for LLMs. You can also refer to [Ray2333/GRM-Gemma-2B-sftreg](https://huggingface.co/Ray2333/GRM-Gemma-2B-sftreg) for a better 2B reward model trained with a hidden states regularization.

## Evaluation
We evaluate this reward model on the [reward model benchmark](https://huggingface.co/spaces/allenai/reward-bench).


|       Model               | Average       |  Chat     |     Chat Hard      |     Safety      |     Reasoning     |   
|:-------------------------:|:-------------:|:---------:|:---------:|:--------:|:-----------:|
|  [**Ray2333/GRM-Gemma-2B-sftreg**](https://huggingface.co/Ray2333/GRM-Gemma-2B-sftreg)(Ours, 2B) | 75.3    |   95.5  |  48.7 |   80.0 | 76.8     |   
|    berkeley-nest/Starling-RM-7B-alpha      (7B)                          |    74.6      |   98      |   43.4   |   88.6  |    74.6    |  
|  **Ray2333/Gemma-2B-rewardmodel-baseline**(Ours, 2B) | 73.7    |   94.1  |  46.1 |  79.6 |  75.0   |  
|     stabilityai/stablelm-zephyr-3b             (3B)                                 |    73.1      |   86.3   |   60.1   |   70.3  |    75.7     | 
|      openbmb/UltraRM-13b             (13B)                                 |    71.3      |   96.1    |   55.3   |   45.8  |    82      | 



## Usage
```
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()
```