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license: apache-2.0 |
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library_name: transformers |
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metrics: |
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- accuracy |
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- code_eval |
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# Flacuna: A Vicuna made of Flan |
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<img src="flacuna5.png" alt="Image" width="200" height="335"> |
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Flacuna was developed by fine-tuning Vicuna on Flan-mini, a comprehensive instruction collection encompassing various tasks. Vicuna is already an excellent writing assistant, and the intention behind Flacuna was to enhance Vicuna's problem-solving capabilities. To achieve this, we curated a dedicated instruction dataset called Flan-mini. |
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| Dataset Name | Source | Dataset Size | |
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|-----------------------------|------------------------|--------------| |
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| Flan2021 | Flan | 388K | |
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| Public Pool of Prompts | Flan | 320K | |
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| Natural instructions v2 | Flan | 200K | |
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| CoT | Flan | 100K | |
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| Code Search | husain2019codesearchnet | 100K | |
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| Code Contest | li2022competition | 50K | |
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| Apps | hendrycksapps2021 | 50K | |
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| GPT4-Alpaca | GPT-4 | 52K | |
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| Code-Alpaca | ChatGPT | 20K | |
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| ShareGPT | ChatGPT | 60K | |
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| Total | - | 1.34M | |
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As a result of this fine-tuning process, Flacuna exhibited notable performance improvements in problem-solving across multiple benchmark datasets, both in few-shot and zero-shot settings. |
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| **Model** | **Size** | **MMLU (5-shot)** | **BBH (3-shot)** | **DROP (3-shot)** | **CRASS (3-shot)** | **HumanEval (0-shot)** | **Avg.** | |
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| --- | --- | --- | --- | --- | --- | --- | --- | |
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| StableVicuna | 13B | 49.2 (+3.0) | 37.5 (+0.4) | 34.3 (-1.0) | 67.5 (+8.7) | 15.9 (+2.5) | 40.9 (+2.7) | |
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| Vicuna | 13B | 50.6 (+4.5) | 37.6 (+0.5) | 32.6 (-3.0) | 60.9 (+2.1) | 11.6 (-1.8) | 38.7 (+0.6) | |
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| Flacuna | 13B | 51.1 (+5.0) | 39.3 (+2.2) | 43.6 (+8.0) | 74.1 (+15.3) | 11.0 (-2.4) | 43.8 (+5.6) | |
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| **Model** | **Size** | **MMLU (0-shot)** | **BBH (0-shot)** | **CRASS (0-shot)** | |
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| --- | --- | --- | --- | --- | |
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| StableVicuna | 13B | 47.5 | 18.5 | 64.2 | |
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| Vicuna | 13B | 48.3 | 28.3 | 65.7 | |
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| Flacuna | 13B | 49.4 | 32.5 | 67.9 | |
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During training, Flacuna employed a maximum input sequence length of 1280. We utilized LoRA for parameter-efficient fine-tuning. |
