This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
:::info Authors:
(1) Lewis Tunstall, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co);
(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;
(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team.
:::
Table of Links
- Abstract and Introduction
- Related Work
- Method
- Experimental Details
- Results and Ablations
- Conclusions and Limitations , Acknowledgements and References
- Appendix
6 CONCLUSIONS AND LIMITATIONS
We consider the problem of alignment distillation from an LLM onto a smaller pretrained model. The method avoids the use of sampling-based approaches like rejection sampling or PPO, and distills conversational capabilities with direct preference optimization (DPO) from a dataset of AI feedback. The resulting model ZEPHYR-7B, based on MISTRAL-7B, sets a new state=of-the-art for 7B parameter chat models, and even outperforms LLAMA2-CHAT-70B on MT-Bench. We hope this approach motivates further exploration of the capacity of smaller, open-models by demonstrating their ability to align to the intent of user interactions.
\ There are several limitations associated with our study. The main one is the use of GPT-4 as an evaluator for the AlpacaEval and MT-Bench benchmarks, which is known to be biased towards models distilled from it, or those that produce verbose, but potentially incorrect responses. Another limitation is examining whether our method scales to much larger models like LLAMA2-70B, where the performance gains are potentially larger.
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7 ACKNOWLEDGEMENTS
We thank Philipp Schmid for many helpful discussions on aligning LLMs, Olivier Dehaene and Nicolas Patry for their assistance with model deployments, Yacine Jernite for his valuable advice on preparing responsible model releases, and Pedro Cuenca for providing feedback on the report. We are grateful to Eric Mitchell, Rafael Rafailov, and Archit Sharma for sharing their insights on DPO. Teven Le Scao for helping with initial experiments. The Mistral, UltraChat, UltraFeedback, Alpaca, and LMSys projects for their support and for releasing great open models. This work would not have been possible without the Hugging Face Training Cluster, and we thank Guillaume Salou and Guillaume Legendre for their help with making the GPUs go brrrr.
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:::info This paper is available on arxiv under CC 4.0 license.
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This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
Writings, Papers and Blogs on Text Models | Sciencx (2024-07-03T14:00:25+00:00) Zephyr: Conclusions and Limitations, Acknowledgements and References. Retrieved from https://www.scien.cx/2024/07/03/zephyr-conclusions-and-limitations-acknowledgements-and-references/
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