---
license: other
library_name: transformers
metrics:
- accuracy
- code_eval
datasets:
- declare-lab/InstructEvalImpact
- declare-lab/flan-mini
---

# Flacuna: A Vicuna made of Flan

[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)

📣 We still have numerous experiments awaiting completion (details are [here](https://arxiv.org/abs//2307.02053)), requiring additional computing resources in our lab. If any industry professionals reading this are willing to provide assistance, please feel free to reach out to us at sporia@sutd.edu.sg.

<img src="https://declare-lab.net/assets/images/logos/flacuna5.png" alt="Image" width="200" height="335">

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
```bash
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](https://github.com/declare-lab/flacuna).

## License
Non-commercial license

## Citation

```bibtex
@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}
}
```