--- language: - en license: apache-2.0 tags: - human feedback - rlhf - preferences - alignment - HALO - halos - dpo - rl datasets: - snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset metrics: - accuracy --- This repo contains the model and tokenizer checkpoints for: - model family [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - optimized with the loss [KTO](https://twitter.com/winniethexu/status/1732839295365554643) - aligned using the [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset) - via 3 iterations of KTO on one epoch of each training partition, each previous iteration's model serving as the reference for the subsequent. **[03/06/2024]**: We are #2 on the (verified) [Alpaca Eval 2.0 Leaderboard](https://tatsu-lab.github.io/alpaca_eval/) scoring **33.23**! To prompt this model, ensure that the format is consistent with that of TuluV2. For example, a prompt should be formatted as follows, where `<|user|>` corresponds to the human's role and `<|assistant|>` corresponds to the LLM's role. The human should speak first: ``` <|user|> Hi! I'm looking for a cake recipe. <|assistant|> What kind of cake? <|user|> Chocolate cake. <|assistant|> ``` Note that a beginning-of-sequence (BOS) token is automatically added at tokenization time and does not have to be added by you. No end-of-sequence (EOS) token is added to the prompt. You may also use our tokenizer's `apply_chat_template` if doing inference with `chatml` set or evaluating generations through non-local clients. For more info on KTO refer to our [code repository](https://github.com/ContextualAI/HALOs) or [blog](https://contextual.ai/better-cheaper-faster-llm-alignment-with-kto/) for more details on the methodology. If you found this work useful, feel free to cite [our work](https://arxiv.org/abs/2402.01306): ``` @techreport{ethayarajh2023halos, author = {Ethayarajh, Kawin and Xu, Winnie, and Jurafsky, Dan and Kiela, Douwe}, title = {Human-Centered Loss Functions (HALOs)}, institution = {Contextual AI}, note = {https://github.com/ContextualAI/HALOs/blob/main/assets/report.pdf}, year = {2023}, } ```