metadata
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
hh-rlhf
This model is a fine-tuned version of 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
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