195 lines
7.3 KiB
Markdown
195 lines
7.3 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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tags:
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- distilabel
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- dpo
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- rlaif
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- rlhf
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- merge
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- mergekit
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datasets:
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- argilla/distilabel-intel-orca-dpo-pairs
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model-index:
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- name: distilabeled-Marcoro14-7B-slerp-full
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 70.65
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 87.55
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 65.33
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 64.21
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 82.0
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 70.66
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=argilla/distilabeled-Marcoro14-7B-slerp-full
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name: Open LLM Leaderboard
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---
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# ⚗️ distilabeled Marcoro14 7B Slerp
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<p align="center">
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<a href="https://github.com/argilla-io/distilabel">
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<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
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</a>
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</p>
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## Introduction
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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).
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The difference between this model and [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp)
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is that this model has been fine-tuned for a whole epoch instead instead of 200 steps, so it has seen the whole dataset.
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## Training details
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As we did with [Notus](https://argilla.io/blog/notus7b/), we wanted a reproducible recipe to test the impact of data quality.
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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):
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```python
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from datasets import load_dataset
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# Instead of this:
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# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")
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# we did this
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dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")
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dataset = dataset.filter(
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lambda r:
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r["status"] != "tie" and
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r["chosen_score"] >= 8 and
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not r["in_gsm8k_train"]
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)
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```
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## Benchmark results
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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`).
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For running the benchmark we used another awesome contribution from Maxime: [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), check it out!
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| Model |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average|
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|-------------------------|------:|------:|---------:|-------:|------:|
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|[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**|
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|[argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp)| **45.4**| 76.47| **65.46**| 47.19| 58.63|
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|[Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) | 44.66| 76.24| 64.15| 45.64| 57.67|
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|[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 |
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### Training Hardware
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We used 1 x A100 80GB in runpod for less than 2 hours.
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## Acknowledgements
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We'd like to thank the amazing open community and in particular:
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* The Intel team for publishing a great open dataset and show how well it worked in the first place
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* Teknium and NousResearch for their awesome work and models.
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* Maxime for sharing such great resources.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_argilla__distilabeled-Marcoro14-7B-slerp-full)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |73.40|
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|AI2 Reasoning Challenge (25-Shot)|70.65|
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|HellaSwag (10-Shot) |87.55|
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|MMLU (5-Shot) |65.33|
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|TruthfulQA (0-shot) |64.21|
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|Winogrande (5-shot) |82.00|
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|GSM8k (5-shot) |70.66|
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