---
model-index:
- name: tulu-v2.5-7b-uf-mean-7b-uf-rm-value
  results: []
datasets:
- allenai/tulu-2.5-preference-data
- allenai/tulu-v2-sft-mixture
language:
- en
base_model: hamishivi/tulu-v2.5-7b-uf-rm
license: apache-2.0
---
<center>
<img src="https://huggingface.co./datasets/allenai/blog-images/resolve/main/tulu-2.5/tulu_25_banner.png" alt="Tulu 2.5 banner image" width="800px"/>
</center>

# Model Card for Tulu V2.5 7B RM - UltraFeedback Value Model

Tulu is a series of language models that are trained to act as helpful assistants.
Tulu V2.5 is a series of models trained using DPO and PPO starting from the [Tulu 2 suite](https://huggingface.co./collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101).
This is a **value** model produced during the PPO training of [this](https://huggingface.co./hamishivi/tulu-v2.5-7b-uf-mean-7b-uf-rm) model.
It was initialised from the [Tulu v2.5 7B UltraFeedback RM](https://huggingface.co./hamishivi/tulu-v2.5-7b-uf-rm).
We release the value model as it may provide a good starting point for additional research or improved decoding with our released PPO models.

At time of writing, you may have to [install transformers from source](https://huggingface.co./docs/transformers/en/installation#install-from-source) to get the `LlamaForTokenClassification` class.

For more details, read the paper:
[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://arxiv.org/abs/2406.09279).


## .Model description

- **Model type:** One model belonging to a suite of RLHF tuned chat models on a mix of publicly available, synthetic and human-created datasets.
- **Language(s) (NLP):** English
- **License:** Apache 2.0.
- **Finetuned from model:** [meta-llama/Llama-2-7b-hf](https://huggingface.co./meta-llama/Llama-2-7b-hf)

### Model Sources

- **Repository:** https://github.com/allenai/open-instruct
- **Dataset:** Prompts used to train this model can be found [here](https://huggingface.co./datasets/allenai/tulu-2.5-preference-data) - specifically the `ultrafeedback_mean_aspects` split.
- **Model Family:** The collection of related models can be found [here](https://huggingface.co./collections/allenai/tulu-v25-suite-66676520fd578080e126f618).

## 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](https://huggingface.co./docs/transformers/main/en/chat_templating) 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](https://huggingface.co./datasets/allenai/tulu-v2-sft-mixture), 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 PPO trainer](https://github.com/hamishivi/EasyLM/blob/main/EasyLM/models/llama/llama_train_ppo.py) built on [EasyLM](https://github.com/young-geng/EasyLM) on the dataset mentioned above.
This model is meant as a research artefact.

### Training hyperparameters

The following hyperparameters were used during overall PPO training:
- learning_rate: 1e-06
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
- KL penalty coefficient: 0.05

## 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}
}
```