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--- |
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license: cc-by-nc-4.0 |
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base_model: mlabonne/Marcoro14-7B-slerp |
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datasets: |
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- argilla/distilabel-intel-orca-dpo-pairs |
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language: |
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- en |
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tags: |
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- distilabel |
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- dpo |
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- rlaif |
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- rlhf |
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- merge |
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- mergekit |
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--- |
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# ⚗️ distilabeled Marcoro14 7B Slerp |
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<p align="center"> |
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<a href="https://github.com/argilla-io/distilabel"> |
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<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> |
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</a> |
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</p> |
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## Introduction |
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This model is a new DPO fine-tune of our new open dataset [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs), on the [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) model. You can find more information of the "distilabeled" dataset used at this repo [argilla/distilabeled-Hermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B/blob/main/README.md#introduction), and visit [distilabel](https://github.com/argilla-io/distilabel). |
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## Training details |
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As we did with [Notus](https://argilla.io/blog/notus7b/), we wanted a reproducible recipe to test the impact of data quality. |
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And we're lucky to have so many amazing folks in the open community contributing reproducible, easy-to-use training scripts and recipes. This time, [Maxime Labonne](https://twitter.com/maximelabonne) had shared a [Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) to fine-tune OpenHermes with DPO and the original Intel's dataset, perfect! We just updated the base model to [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp), and applied the same dataset recipe we used for [argilla/distilabeled-Hermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B/blob/main/README.md#introduction): |
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```python |
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from datasets import load_dataset |
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# Instead of this: |
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# dataset = load_dataset("Intel/orca_dpo_pairs", split="train") |
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# we did this |
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dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train") |
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dataset = dataset.filter( |
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lambda r: |
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r["status"] != "tie" and |
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r["chosen_score"] >= 8 and |
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not r["in_gsm8k_train"] |
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) |
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``` |
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## Benchmark results |
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For benchmarking we used the famous "Nous" or "Teknium" benchmark. You can find below an overview, including our first experiment with a less ambitious dataset filtering (removing ties and `score>5`). |
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For running the benchmark we used another awesome contribution from Maxime: [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), check it out! |
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| Model |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average| |
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|-------------------------|------:|------:|---------:|-------:|------:| |
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|[argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp)| **45.4**| **76.47**| **65.46**| **47.19**| **58.63**| |
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|[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) | 44.66| 76.24| 64.15| 45.64| 57.67| |
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|[argilla/distilabeled-Hermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B) | 44.64 | 73.35 | 55.96 | 42.21 | 54.04 | |
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### Training Hardware |
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We used 1 x A100 80GB in runpod for less than 1 hour. |
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## Acknowledgements |
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We'd like to thank the amazing open community and in particular: |
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* The Intel team for publishing a great open dataset and show how well it worked in the first place |
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* Teknium and NousResearch for their awesome work and models. |
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* Maxime for sharing such great resources. |
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