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βš—οΈ distilabeled Marcoro14 7B Slerp

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Introduction

This model is a new DPO fine-tune of our new open dataset argilla/distilabel-intel-orca-dpo-pairs, on the 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, and visit distilabel.

The difference between this model and 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, 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 had shared a Colab to fine-tune OpenHermes with DPO and the original Intel's dataset, perfect! We just updated the base model to mlabonne/Marcoro14-7B-slerp, and applied the same dataset recipe we used for argilla/distilabeled-Hermes-2.5-Mistral-7B:

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, check it out!

Model AGIEval GPT4ALL TruthfulQA Bigbench Average
argilla/distilabeled-Marcoro14-7B-slerp-full 45.17 76.59 64.68 48.15 58.65
argilla/distilabeled-Marcoro14-7B-slerp 45.4 76.47 65.46 47.19 58.63
Marcoro14-7B-slerp 44.66 76.24 64.15 45.64 57.67
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

Detailed results can be found here

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
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