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--- |
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license: cc |
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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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datasets: |
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- declare-lab/InstructEvalImpact |
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--- |
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# Flacuna: A Vicuna made of Flan |
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[Paper](https://arxiv.org/abs//2307.02053) | [Model](https://huggingface.co/declare-lab/flacuna-13b-v1.0) | [Dataset](https://huggingface.co/datasets/declare-lab/flan-mini) |
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<img src="https://declare-lab.net/assets/images/logos/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 | HF/code_search_net | 100K | |
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| Code Contest | HF/deepmind/code_contests | 50K | |
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| Apps | HF/codeparrot/apps | 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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## Problem Solving Ability |
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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 is a 13B checkpoint of LLaMA and employed a maximum input sequence length of 1280. We utilized LoRA for parameter-efficient fine-tuning. |
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## Chatbot / Writing Assistant |
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While Flacuna primarily excels in problem-solving tasks, we made efforts to maintain the impressive writing and chatting ability of Vicuna. To achieve this, we incorporated conversational datasets generated by GPT-4, such as GPT-4-Alpaca and ShareGPT, into the Flan-mini collection. |
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To use Flacuna as a chatbot or writing assistant, we recommend you use the following template: |
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``` |
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A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {definition of the task}.\n\n |
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{question}\n |
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Output: ASSISTANT: |
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``` |
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**Please note that we still recommend using Vicuna as your preferred Chatbot or Writing Assistant, over Flacuna. Flacuna's primary strength lies in problem-solving tasks, making it ideal for such applications.** |
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The following table presents the writing performance of Flacuna on the IMPACT dataset, which is a component of the InstructEval evaluation suite. The generated responses have been evaluated by ChatGPT, and their relevance and coherence have been scored on a scale of 1 to 5. |
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| **Model** | **Size** | **Informative Rel.** | **Informative Coh.** | **Professional Rel.** | **Professional Coh.** | **Argumentative Rel.** | **Argumentative Coh.** | **Creative Rel.** | **Creative Coh.** | **Avg. Rel.** | **Avg. Coh.** | |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
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| ChatGPT | - | 3.34 | 3.98 | 3.88 | 3.96 | 3.96 | 3.82 | 3.92 | 3.94 | 3.78 | 3.93 | |
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| Flan-Alpaca | 11B | 3.56 | 3.46 | 3.54 | 3.70 | 3.22 | 3.28 | 3.70 | 3.40 | 3.51 | 3.46 | |
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| Flan-T5 | 11B | 2.64 | 3.24 | 2.62 | 3.22 | 2.54 | 3.40 | 2.50 | 2.72 | 2.58 | 3.15 | |
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| Dolly-V2 | 12B | 3.54 | 3.64 | 2.96 | 3.74 | 3.66 | 3.20 | 3.02 | 3.18 | 3.30 | 3.44 | |
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| StableVicuna | 13B | 3.54 | 3.64 | 2.96 | 3.74 | 3.30 | 3.20 | 3.02 | 3.18 | 3.21 | 3.44 | |
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| Vicuna | 13B | 3.60 | 3.96 | 3.74 | 3.82 | 3.82 | 3.56 | 3.82 | 3.92 | 3.75 | 3.82 | |
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| Flacuna | 13B | 3.02 | 3.42 | 3.48 | 3.52 | 3.38 | 3.02 | 3.92 | 3.80 | 3.45 | 3.44 | |
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## Basic Usage |
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```bash |
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git clone https://huggingface.co/declare-lab/flacuna-13b-v1.0 |
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cd flacuna-13b-v1.0 |
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python flacuna.py |
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``` |
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## Training or Fine-tuning Flacuna |
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The trainer codes are available here: [https://github.com/declare-lab/flacuna](https://github.com/declare-lab/flacuna). |
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## Citation |
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```bibtex |
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@misc{ghosal2023flacuna, |
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title={Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning}, |
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author={Deepanway Ghosal and Yew Ken Chia and Navonil Majumder and Soujanya Poria}, |
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year={2023}, |
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eprint={2307.02053}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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