README.md

---
license: mit
base_model: vicgalle/gpt2-open-instruct-v1
tags:
- generated_from_trainer
- Transformers
- GPT2
model-index:
- name: hh-rlhf
  results: []
datasets:
- Anthropic/hh-rlhf
- hakurei/open-instruct-v1
tokenizers:
- GPT2Tokenizer
language:
- en
library_name: transformers
metrics:
- bleu
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hh-rlhf

This model is a fine-tuned version of [vicgalle/gpt2-open-instruct-v1](https://huggingface.co/vicgalle/gpt2-open-instruct-v1) on an subset (15k) of the Anthropic/hh-rlhf dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1534

## Model description

GPT2 open instruct was trained on the open-instruct dataset fully. The reimagines one LM head as a partial rhlf adapter, with subtle reinforcements. 
## Intended uses & limitations

Intended to study the intersection of instruct models and prompting that focuses on subtle exchanges of prompting. This probably needs to be refined substantially at this point.

## Training and evaluation data
```python
Train dataset size: 15000
Test dataset size: 500
Dataset({
    features: ['chosen', 'rejected'],
    num_rows: 15000
})
Dataset({
    features: ['chosen', 'rejected'],
    num_rows: 500
})
```
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3108        | 1.0   | 7500  | 2.1799          |
| 2.265         | 2.0   | 15000 | 2.1632          |
| 2.2507        | 3.0   | 22500 | 2.1567          |
| 2.2519        | 4.0   | 30000 | 2.1534          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3