---
license: mit
datasets:
- hendrydong/preference_700K
pipeline_tag: text-classification
---

# Introduction
This is a breward model (based on Gemma-2b-it) trained with BT loss using [hendrydong/preference_700K](https://huggingface.co/datasets/hendrydong/preference_700K) 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) | 75.1    |   95.5  |  48.2 |   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/GRM-llama3-8B-distill')
reward_model = AutoModelForSequenceClassification.from_pretrained(
                'Ray2333/GRM-llama3-8B-distill',
                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": 'max_length', "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()
```