---
license: cc-by-nc-4.0
base_model: mlabonne/Marcoro14-7B-slerp
datasets:
  - argilla/distilabel-intel-orca-dpo-pairs
language:
  - en
tags:
  - distilabel
  - dpo
  - rlaif
  - rlhf
  - merge
  - mergekit
---
# ⚗️ distilabeled Marcoro14 7B Slerp


<p align="center">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>


## 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.