license: cc
library_name: transformers
metrics:
- accuracy
- code_eval
datasets:
- declare-lab/InstructEvalImpact
Flacuna: A Vicuna made of Flan
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.
| Dataset Name | Source | Dataset Size |
|---|---|---|
| Flan2021 | Flan | 388K |
| Public Pool of Prompts | Flan | 320K |
| Natural instructions v2 | Flan | 200K |
| CoT | Flan | 100K |
| Code Search | HF/code_search_net | 100K |
| Code Contest | HF/deepmind/code_contests | 50K |
| Apps | HF/codeparrot/apps | 50K |
| GPT4-Alpaca | GPT-4 | 52K |
| Code-Alpaca | ChatGPT | 20K |
| ShareGPT | ChatGPT | 60K |
| Total | - | 1.34M |
Problem Solving Ability
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.
| Model | Size | MMLU (5-shot) | BBH (3-shot) | DROP (3-shot) | CRASS (3-shot) | HumanEval (0-shot) | Avg. |
|---|---|---|---|---|---|---|---|
| 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) |
| 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) |
| 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) |
| Model | Size | MMLU (0-shot) | BBH (0-shot) | CRASS (0-shot) |
|---|---|---|---|---|
| StableVicuna | 13B | 47.5 | 18.5 | 64.2 |
| Vicuna | 13B | 48.3 | 28.3 | 65.7 |
| Flacuna | 13B | 49.4 | 32.5 | 67.9 |
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.
Chatbot / Writing Assistant
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. To use Flacuna as a chatbot or writing assistant, we recommend you use the following template:
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
{question}\n
Output: ASSISTANT:
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.
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.
| Model | Size | Informative Rel. | Informative Coh. | Professional Rel. | Professional Coh. | Argumentative Rel. | Argumentative Coh. | Creative Rel. | Creative Coh. | Avg. Rel. | Avg. Coh. |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ChatGPT | - | 3.34 | 3.98 | 3.88 | 3.96 | 3.96 | 3.82 | 3.92 | 3.94 | 3.78 | 3.93 |
| Flan-Alpaca | 11B | 3.56 | 3.46 | 3.54 | 3.70 | 3.22 | 3.28 | 3.70 | 3.40 | 3.51 | 3.46 |
| Flan-T5 | 11B | 2.64 | 3.24 | 2.62 | 3.22 | 2.54 | 3.40 | 2.50 | 2.72 | 2.58 | 3.15 |
| Dolly-V2 | 12B | 3.54 | 3.64 | 2.96 | 3.74 | 3.66 | 3.20 | 3.02 | 3.18 | 3.30 | 3.44 |
| StableVicuna | 13B | 3.54 | 3.64 | 2.96 | 3.74 | 3.30 | 3.20 | 3.02 | 3.18 | 3.21 | 3.44 |
| Vicuna | 13B | 3.60 | 3.96 | 3.74 | 3.82 | 3.82 | 3.56 | 3.82 | 3.92 | 3.75 | 3.82 |
| Flacuna | 13B | 3.02 | 3.42 | 3.48 | 3.52 | 3.38 | 3.02 | 3.92 | 3.80 | 3.45 | 3.44 |
Basic Usage
git clone https://huggingface.co/declare-lab/flacuna-13b-v1.0
cd flacuna-13b-v1.0
python flacuna.py
Training or Fine-tuning Flacuna
The trainer codes are available here: https://github.com/declare-lab/flacuna.
Citation
@misc{ghosal2023flacuna,
title={Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning},
author={Deepanway Ghosal and Yew Ken Chia and Navonil Majumder and Soujanya Poria},
year={2023},
eprint={2307.02053},
archivePrefix={arXiv},
primaryClass={cs.CL}
}