language, license, tags, datasets, model-index

language	license	tags	datasets	model-index
en	apache-2.0	distilabel dpo rlaif rlhf merge mergekit	argilla/distilabel-intel-orca-dpo-pairs	name results distilabeled-Marcoro14-7B-slerp-full task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 70.65 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 87.55 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 65.33 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 64.21 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 82.0 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 70.66 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full Open LLM Leaderboard

⚗️ distilabeled Marcoro14 7B Slerp

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