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README.md

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+---
+license: apache-2.0
+datasets:
+- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
+pipeline_tag: text-generation
+---
+
+### Dataset:
+Training dataset: [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset)
+
+We utilize ONLY the prompts from [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized); **no external LLM responses used**.
+
+### Methodology:
+  1. Generate five response variations for each prompt from a subset of 20,000 using the LLM - to start, we used [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
+  2. Apply [PairRM](https://huggingface.co/llm-blender/PairRM) for response reranking.
+  3. Update the LLM by applying Direct Preference Optimization (DPO) on the top (chosen) and bottom (rejected) responses.
+  4. Use this LLM as the base model for the next iteration, repeating three times in total.
+ 
+This overview provides a high-level summary of our approach. 
+We plan to release more detailed results and findings in the coming weeks on the [Snorkel blog.](https://snorkel.ai/blog/)
+
+### Training recipe:
+- The provided data is formatted to be compatible with the Hugging Face's [Zephyr recipe](https://github.com/huggingface/alignment-handbook/tree/main/recipes/zephyr-7b-beta).
+We executed the n_th DPO iteration using the "train/test_iteration_{n}".
+
+### Key Premises:
+- **Specialization Requirement**: For most enterprise use cases, using LLMs "off-the-shelf" falls short of production quality, necessitating additional fine-tuning and alignment.
+- **Ease of Model Building**: Creating ranking/scoring/classification models is simpler than developing high-quality, manually annotated datasets for long-form responses.
+- **Alignment Recipe**: Using smaller but specialized teacher models (reward models) can incrementally align LLMs towards specific axes.
+
+### Applications:
+Unlike our customers, who have very specific use cases to align LLMs to,
+the AlpacaEval 2.0 leaderboard measures the ability of LLMS to follow user instructions.
+With this demonstration, we focus on the general approach to alignment. 
+Thus, we use a general-purpose reward model - the performant [PairRM model](https://huggingface.co/llm-blender/PairRM).
+We use the [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model as our base LLM.
+
+For interest in building your **specialized internal reward models
+that reflect your enterprises' needs**, please contact the Snorkel AI team or consider attending our
+[**Enterprise LLM Summit: Building GenAI with Your Data on January 25, 2024**](https://snorkel.ai/event/enterprise-llm-summit/)
+to learn more about "Programmatically scaling human preferences and alignment in GenAI".
+
+### Result:
+On [**Alpaca-Eval 2.0**](https://tatsu-lab.github.io/alpaca_eval/):
+- The base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) scored **14.72**.
+
+After applying the above methodology:
+- This model scored **30.22** - ranked 3rd and the highest for an open-source base model at the time of publication.
+- When post-processing the model outputs with PairRM-best-of-16, which involved generating 16 responses and selecting the highest-scoring response by PairRM, we scored **34.86** - ranked 2nd.
+The best model on the leaderboard is "gpt-4-turbo", which is also the judge of optimal responses.
+
+We recognize that the Alpaca-Eval 2.0 benchmark does not entirely capture the full range of capabilities and performances of LLMs.
+However, in our current work, where the goal is to align with general "human preferences," Alpaca-Eval 2.0 serves as a suitable and representative benchmark.
+Moving forward, we anticipate further contributions from the community regarding new alignment axes, and conduct evaluations using other appropriate benchmarks.
+
+The Alpaca-Eval 2.0 evaluator, "gpt-4-turbo," exhibits a bias towards longer responses. 
+This tendency might also be present in our chosen reward model, resulting in our model producing lengthier responses after DPO iterations,
+which can be among the factors to our higher ranks on the leaderboard.
+Future work could include measures to control response length and other relevant metrics.
+
+### Limitations:
+The model is a quick demonstration that the LLMs can be programmatically aligned using smaller specialized reward models. 
+It does not have any moderation mechanisms. 
+We look forward to continuing to engage with the research community and our customers exploring optimal methods for getting models to respect guardrails, 
+allowing for deployment in environments requiring moderated outputs.
+
+### Contemporary Work and Acknowledgements:
+- The Mistral AI Team for developing and releasing the advanced Mistral-7B-Instruct-v0.2 model.
+- The author of the [Direct Preference Optimization paper](https://arxiv.org/abs/2305.18290) for the innovative approach
+- The author of the [Pairwise Reward Model for LLMs paper](https://arxiv.org/abs/2306.02561) for the powerful general-purpose reward model
+- The HuggingFace team for the DPO implementation under [The Alignment Handbook](https://github.com/huggingface/alignment-handbook)
+- We would also like to acknowledge contemporary work published independently on arXiv on 2024-01-18 by Meta & NYU (Yuan, et al) in a paper called [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020), 
+which proposes a similar general approach for creating alignment pairs from a larger set of candidate responses, but using the LLM as the reward model.
+While this may work for general-purpose models, our experience has shown that task-specific reward models guided by SMEs are necessary for most 
+enterprise applications of LLMs for specific use cases, which is why we focus on the use of external reward models.
+
+### The Snorkel AI Team
+Hoang Tran, Chris Glaze, Braden Hancock

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all_results.json

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config.json

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+  "vocab_size": 32000
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eval_results.json

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generation_config.json

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