Model Card for Llama 3 Tulu V2 8B RM - UltraFeedback
Tulu is a series of language models that are trained to act as helpful assistants. This is a 8B reward model used for PPO training trained on the UltraFeedback dataset.
For more details, read the paper: Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback.
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Note that Llama 3 is released under the Meta Llama 3 community license, included here under llama_3_license.txt.
Performance
We evaluate the model on RewardBench:
| Model | Score | Chat | Chat Hard | Safety | Reasoning |
|---|---|---|---|---|---|
| Llama 3 Tulu 2 8b UF RM (this model) | 73.6 | 95.3 | 59.2 | 57.9 | 82.1 |
| Llama 3 Tulu 2 70b UF RM | 71.0 | 86.3 | 56.1 | 58.9 | 82.7 |
Model description
- Model type: A reward model trained on UltraFeedback, designed to be used in RLHF training.
- Language(s) (NLP): English
- License: Apache 2.0.
- Finetuned from model: allenai/llama-3-tulu-2-8b
Model Sources
- Repository: https://github.com/allenai/open-instruct
- Dataset: Data used to train this model can be found here - specifically the
ultrafeedback_mean_aspectssplit.
Input Format
The model is trained to use the following format (note the newlines):
<|user|>
Your message here!
<|assistant|>
For best results, format all inputs in this manner. Make sure to include a newline after <|assistant|>, this can affect generation quality quite a bit.
We have included a chat template in the tokenizer implementing this template.
Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the Tulu V2 mix dataset, which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs. We then further trained the model with a Jax RM trainer built on EasyLM on the dataset mentioned above. This model is meant as a research artefact.
Training hyperparameters
The following hyperparameters were used during PPO training:
- learning_rate: 1e-06
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear cooldown to 1e-05.
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
Citation
If you find Tulu 2.5 is useful in your work, please cite it with:
@misc{ivison2024unpacking,
title={{Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback}},
author={{Hamish Ivison and Yizhong Wang and Jiacheng Liu and Ellen Wu and Valentina Pyatkin and Nathan Lambert and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi}}
year={2024},
eprint={2406.09279},
archivePrefix={arXiv},
primaryClass={cs.CL}
}