---
license: apache-2.0
language:
- en
---
# InsTagger

**InsTagger** is an tool for automatically providing instruction tags by distilling tagging results from **InsTag**.

InsTag aims analyzing supervised fine-tuning (SFT) data in LLM aligning with human preference. For local tagging deployment, we release InsTagger, fine-tuned on InsTag results, to tag the queries in SFT data. Through the scope of tags, we sample a 6K subset of open-resourced SFT data to fine-tune LLaMA and LLaMA-2 and the fine-tuned models TagLM-13B-v1.0 and TagLM-13B-v2.0 outperform many open-resourced LLMs on MT-Bench.


### Model Description

- **Model type:** Auto-regressive Models
- **Language(s) (NLP):** English
- **License:** apache-2.0
- **Finetuned from model:** LLaMa-2

### Model Sources [optional]

- **Repository:** [https://github.com/OFA-Sys/InsTag](https://github.com/OFA-Sys/InsTag)
- **Paper:** [Arxiv](https://arxiv.org/pdf/2308.07074.pdf)
- **Demo:** [ModelScope Demo](https://www.modelscope.cn/studios/lukeminglkm/instagger_demo/summary)

## Uses

This model is directly developed with [FastChat](https://github.com/lm-sys/FastChat). So it can be easily infer or serve with FastChat selecting the vicuna template.