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Deita: Data-Efficient Instruction Tuning for Alignment [ICLR2024]
| Date | Stars |
|---|---|
| 2026-07-31 | 600 |
| 2026-08-01 | 600 |
| 2026-08-06 | 600 |
Today
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# Deita
<p align="center">
<img src="./assets/logo-final.png" width="600">
</p>
<p align="center">
🤗 <a href="https://huggingface.co/collections/hkust-nlp/deita-6569c198c174808d94cf5bd4">HF Repo</a>
📄 <a href="https://arxiv.org/abs/2312.15685">Paper</a>
📚 <a href="https://huggingface.co/datasets/hkust-nlp/deita-6k-v0">6K Data</a>
📚 <a href="https://huggingface.co/datasets/hkust-nlp/deita-10k-v0">10K Data</a>
</p>
Welcome to Deita (**D**ata-**E**fficient **I**nstruction **T**uning for **A**lignment) Project!
We will continue to update, please stay tuned!
## What is Deita?
Deita is an open-sourced project designed to facilitate **Automatic Data Selection** for instruction tuning in Large Language Models (LLMs).
It includes:
- **Open-sourced Toolkits** for automatic data selection in instruction tuning
- **Deita Datasets**: A series of extremely *lightweight*, high-quality alignment SFT data. We release 6k-sized and 10k-sized datasets in the first release
- **Deita Models**: A series of powerful models on par with SOTA chat LLMs with an extremely efficient instruction tuning Process. Deita models can be obained by training with 10x less instruction tuning data compared with other SOTA LLMs
## News
- :fire: [03/2024] Our datasets have been used by Huggingface to creat the [Zephyr Gemma Model](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-gemma-65e1fd82d26b426e3e63d956).
- 📄 [01/2024] Deita paper [What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning](https://arxiv.org/abs/2312.15685) has been accepted by ICLR2024!
- :fire: [01/2024] [Deita pipelines](#deita-pipelines) have been released! With one line code and configurations, a high-quality data subset for alignment can be selected.
- 📚 [01/2024] Our scorer datasets [deita-complexity-scorer-data](https://huggingface.co/datasets/hkust-nlp/deita-complexity-scorer-data) and [deita-quality-scorer-data](https://huggingface.co/datasets/hkust-nlp/deita-quality-scorer-data) have been released.
- :fire: [12/2023] We release the first collection of the Deita resources [here](https://huggingface.co/collections/hkust-nlp/deita-6569c198c174808d94cf5bd4), which include a series of extremely lightweight, effective sft datasets, the data complexity/quality scorer models, as well as the resulted deita chat models.
## Performance
:bell: Still curious about how far a small amount of high-quality data can lead LLMs?
Deita may provide an answer for you:
**🔦 Highlights**
| Model | Align | Data Size | MT-Bench | AlpacaEval(%) |
|------------------------------------------------|--------------|------------|----------|---------------|
| Zephyr-7B-sft | SFT | 200K | 5.32 | 75.12 |
| $\text{Zephyr-7B-}\beta$ | SFT + DPO | 200K SFT + 60K DPO | 7.34 | 90.60 |
| OpenChat-3.5 | C-RLFT | >> 70K C-RLFT | 7.81 | 88.51 |
| Starling-7B | C-RLFT + APA | >> 70K C-RLFT + 183K APA | 8.09 | 91.99 |
| Tulu-2-13B | SFT | 326K | 6.70 | 78.90 |
| Tulu-2-13B+DPO | SFT + DPO | 326K SFT + 60K DPO | 7.00 | 89.50 |
| LLaMA2-13B-Chat | SFT + PPO | -- | 6.65 | 81.09 |
| WizardLM-13B-v1.2 | SFT | >70K | 7.09 | 89.17 |
| Vicuna-13B-v1.5 | SFT | >125K | 6.57 | 78.80 |
| DEITA-7B-v1.0 (6K) | SFT | 6K | 7.22 | 80.78 |
| DEITA-7B-v1.0-sft | SFT | 10K | 7.32 | 81.67 |
| DEITA-7B-v1.0 | SFT + DPO | 6K SFT + 1Excerpt of 18,845 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9db9b582175314a0, topic:instruction-tuning, topic:alignment, desc:instruction tuning
