---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-classification
tags:
- pytorch
- reward_model
- transformers
- RLHF
---

This is part of the Chai reward-model series, using the GPT2 architecture with a classification head, optimising for a user accepting the completion generated by the base model.

Its training dataset consists of purely user-generated content [retry_and_continue_50m_reward_model](https://huggingface.co/datasets/ChaiML/retry_and_continue_50m_reward_model), where a user has the option to decline the generated response via the retry button or end the conversation.

## Model details
- Developed by [Chai Research](https://www.chai-research.com/)
- Model type: Transformer-based Classification Model
- Language: English
- License: cc-by-nc-4.0
- Contact: to ask questions about this model, join the [Chai Discord](https://discord.com/invite/4KPHkeG6VX). For general correspondence: [hello@chai-research.com](mailto:hello@chai-research.com?subject=Huggingface%20Model%20Inquiry)

## Uses and limitations
### Intended use
### Out-of-scope use
### How to use

This reward model can be loaded using the `AutoModelForSequenceClassification` functionality, with a GPT2 tokenizer where the `pad_token_id` is set to the EOS token id, padding sides need to be set according to the configurations used during model training.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForSequenceClassification.from_pretrained("ChaiML/gpt2_base_retry_and_continue_5m_reward_model")
tokenizer.pad_token_id = 50256
tokenizer.truncation_side = ‘left’
tokenizer.padding_side = ‘right’
tokens = self.eval_tokenizer(candidates, return_tensors='pt', return_attention_mask=True, padding='longest', truncation=True, max_length=256)
reward = model(**tokens).logits
```