README.md

---
language:
- en
license: apache-2.0
tags:
- distilabel
- dpo
- rlaif
- rlhf
- merge
- mergekit
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
model-index:
- name: distilabeled-Marcoro14-7B-slerp-full
  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: 70.65
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
      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: 87.55
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
      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: 65.33
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
      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: 64.21
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
      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: 82.0
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
      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: 70.66
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
      name: Open LLM Leaderboard
---
# ⚗️ 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).

The difference between this model and [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co./argilla/distilabeled-Marcoro14-7B-slerp)
is that this model has been fine-tuned for a whole epoch instead instead of 200 steps, so it has seen the whole dataset.

## 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-full](https://huggingface.co./argilla/distilabeled-Marcoro14-7B-slerp-full)|  45.17|  **76.59**|     64.68|   **48.15**|  **58.65**|
|[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 2 hours.

## 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-full)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |73.40|
|AI2 Reasoning Challenge (25-Shot)|70.65|
|HellaSwag (10-Shot)              |87.55|
|MMLU (5-Shot)                    |65.33|
|TruthfulQA (0-shot)              |64.21|
|Winogrande (5-shot)              |82.00|
|GSM8k (5-shot)                   |70.66|