--- language: - en license: cc-by-nc-4.0 tags: - distilabel - dpo - rlaif - rlhf - merge - mergekit datasets: - argilla/distilabel-intel-orca-dpo-pairs base_model: mlabonne/Marcoro14-7B-slerp model-index: - name: distilabeled-Marcoro14-7B-slerp results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 70.73 name: normalized accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.47 name: normalized accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.22 name: accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.1 source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.08 name: accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.19 name: accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp name: Open LLM Leaderboard --- # ⚗️ distilabeled Marcoro14 7B Slerp

Built with Distilabel

## Introduction 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). ## Training details As we did with [Notus](https://argilla.io/blog/notus7b/), we wanted a reproducible recipe to test the impact of data quality. 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): ```python from datasets import load_dataset # Instead of this: # dataset = load_dataset("Intel/orca_dpo_pairs", split="train") # we did this dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train") dataset = dataset.filter( lambda r: r["status"] != "tie" and r["chosen_score"] >= 8 and not r["in_gsm8k_train"] ) ``` ## Benchmark results 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`). For running the benchmark we used another awesome contribution from Maxime: [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), check it out! | Model |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average| |-------------------------|------:|------:|---------:|-------:|------:| |[argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co./argilla/distilabeled-Marcoro14-7B-slerp)| **45.4**| **76.47**| **65.46**| **47.19**| **58.63**| |[Marcoro14-7B-slerp](https://huggingface.co./mlabonne/Marcoro14-7B-slerp) | 44.66| 76.24| 64.15| 45.64| 57.67| |[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 | ### Training Hardware We used 1 x A100 80GB in runpod for less than 1 hour. ## Acknowledgements We'd like to thank the amazing open community and in particular: * The Intel team for publishing a great open dataset and show how well it worked in the first place * Teknium and NousResearch for their awesome work and models. * Maxime for sharing such great resources. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_argilla__distilabeled-Marcoro14-7B-slerp) | Metric |Value| |---------------------------------|----:| |Avg. |73.63| |AI2 Reasoning Challenge (25-Shot)|70.73| |HellaSwag (10-Shot) |87.47| |MMLU (5-Shot) |65.22| |TruthfulQA (0-shot) |65.10| |Winogrande (5-shot) |82.08| |GSM8k (5-shot) |71.19|