metadata
license: apache-2.0
library_name: transformers
metrics:
- accuracy
- code_eval
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 | husain2019codesearchnet | 100K |
| Code Contest | li2022competition | 50K |
| Apps | hendrycksapps2021 | 50K |
| GPT4-Alpaca | GPT-4 | 52K |
| Code-Alpaca | ChatGPT | 20K |
| ShareGPT | ChatGPT | 60K |
| Total | - | 1.34M |
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 employed a maximum input sequence length of 1280. We utilized LoRA for parameter-efficient fine-tuning.