Create README.md

README.md

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+---
+model-index:
+- name: tulu-v2.5-ppo-13b-uf-mean-70b-uf-rm
+  results: []
+datasets:
+- allenai/tulu-2.5-preference-data
+- allenai/tulu-v2-sft-mixture
+language:
+- en
+base_model: allenai/tulu-2-dpo-13b
+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 PPO 13B - UltraFeedback Mean w. 70B UltraFeedback RM
+
+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 model is trained on the UltraFeedback dataset (using the per-aspect/fine-grained scores for deciding chosen and rejected) using PPO.
+We used a 70B RM trained on the UltraFeedback dataset, and then used the UltraFeedback prompts during PPO training.
+
+For more details, read the paper:
+[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://link.todo).
+
+
+## .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-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)
+
+### Model Sources
+
+- **Repository:** https://github.com/allenai/open-instruct
+- **Dataset:** Data 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. Only the prompts were used.
+- **Model Family:** The collection of related models can be found [here](https://huggingface.co/collections/allenai/tulu-v25-suite-66676520fd578080e126f618).
+- **Reward Model:** The reward model used during PPO training can be found [here](https://huggingface.co/allenai/tulu-v2.5-70b-uf-rm), and the data used to train it [here](https://huggingface.co/datasets/allenai/tulu-2.5-preference-data) - specifically the `ultrafeedback_mean_aspects` split.
+
+
+## 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 aligned the model with a [Jax DPO trainer](https://github.com/hamishivi/EasyLM/blob/main/EasyLM/models/llama/llama_train_dpo.py) built on [EasyLM](https://github.com/young-geng/EasyLM) on the dataset mentioned above.
+
+## Bias, Risks, and Limitations
+
+The Tulu models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). 
+It is also unknown what the size and composition of the corpus was used to train the base Llama 2 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during 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: 0.0325 (we found the larger RM benefited from a smaller KL penalty)
+
+## 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},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```