Duplicate from alvarobartt/Mistral-7B-v0.1-ORPO

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README.md

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+---
+license: apache-2.0
+datasets:
+- alvarobartt/dpo-mix-7k-simplified
+- argilla/dpo-mix-7k
+base_model: mistralai/Mistral-7B-v0.1
+language:
+- en
+library_name: peft
+pipeline_tag: text-generation
+inference: false
+tags:
+- orpo
+- qlora
+- trl
+---
+
+## ORPO fine-tune of Mistral 7B v0.1 with DPO Mix 7K
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/60f0608166e5701b80ed3f02/hRyhnTySt-KQ0gnnoclSm.jpeg)
+
+> Stable Diffusion XL "A capybara, a killer whale, and a robot named Ultra being friends"
+
+This is an ORPO fine-tune of [`mistralai/Mistral-7B-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1) with
+[`alvarobartt/dpo-mix-7k-simplified`](https://huggingface.co/datasets/alvarobartt/dpo-mix-7k-simplified).
+
+⚠️ Note that the code is still experimental, as the `ORPOTrainer` PR is still not merged, follow its progress
+at [🤗`trl` - `ORPOTrainer` PR](https://github.com/huggingface/trl/pull/1435).
+
+## About the fine-tuning
+
+In order to fine-tune [`mistralai/Mistral-7B-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1) using ORPO, the branch
+`orpo` from [🤗`trl`](https://github.com/huggingface/trl) has been used, thanks to the invaluable and quick contribution of
+@kashif.
+
+ORPO stands for Odds Ratio Preference Optimization, and defines a new paradigm on fine-tuning LLMs, “combining” both the SFT
+and the PPO/DPO stage into a single stage, thanks to the proposed loss function starting off from a preference dataset i.e.
+chosen-rejected pairs.
+
+Some key features about ORPO:
+- ⚡️ Faster to train as it’s now a single stage fine-tuning
+- 👨🏻‍🏫 Requires preference data i.e. (prompt, chosen, rejected)-like datasets
+- ⬇️ Less memory than PPO/DPO as doesn’t need a reference model
+- 🏆 SOTA results for Phi-2 (2.7B), Llama-2 (7B), and Mistral (7B) when fine-tuned using single-turn UltraFeedback
+
+Some notes on the experiments mentioned in the paper:
+- 📌 Up to 7B parameter LLMs were fine-tuned, achieving better performance compared to 7B counterparts and even 13B LLMs
+- 📌 Not yet trained with multi-turn datasets as Capybara (may be an interesting experiment to run)
+- 📌 For OPT models fine-tuned with HH-RLHF from Anthropic, truncated and padded to 1024 tokens, filtering out filtering the prompts with > 1024 tokens
+- 📌 For Phi-2, Mistral (7B) and Llama 2 (7B), or UltraFeedback from OpenBMB (truncated and padded to 2048 tokens), filtering out filtering the prompts with > 1024 tokens
+- 📌 Fine-tuned for 10 epochs, and using the evaluation loss as the metric for selecting the best models
+
+For more information about ORPO, I highly recommend reading their paper titled [`ORPO: Monolithic Preference Optimization without Reference Model`](https://huggingface.co/papers/2403.07691),
+as it contains a lot of information and details not only on the ORPO method, but also on the experiment they ran, the results they got, and much more.
+
+📅 Fine-tuning code will be shared soon, stay tuned!
+
+## About the dataset
+
+The dataset used for this fine-tune is [`alvarobartt/dpo-mix-7k-simplified`](https://huggingface.co/datasets/alvarobartt/dpo-mix-7k-simplified),
+which is a simplified version of [`argilla/dpo-mix-7k`](https://huggingface.co/datasets/argilla/dpo-mix-7k).
+
+The simplification comes from the fact that the `prompt` column is detached from both the `chosen` and `rejected`
+columns so that there's no need for extra pre-processing while applying the chat template to the dataset before the
+fine-tuning. So on, the dataset remains as is, with an additional column for the `prompt`.
+
+The dataset is a small cocktail combining Argilla's latest efforts on DPO datasets, mixing the following datasets:
+
+* [`argilla/distilabel-capybara-dpo-7k-binarized`](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized)
+* [`argilla/distilabel-intel-orca-dpo-pairs`](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs)
+* [`argilla/ultrafeedback-binarized-preferences-cleaned`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)
+
+The samples have been randomly selected from the original datasets with a proportion of 0.33 each, as can be seen via the `dataset` column of the dataset.
+
+For more information about the original dataset check [the `README.md` file of `argilla/dpo-mix-7k`](https://huggingface.co/datasets/argilla/dpo-mix-7k/blob/main/README.md).

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config.json

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