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
• Paper: Arxiv
• Demo: ModelScope Demo

Uses

This model is directly developed with FastChat. So it can be easily infer or serve with FastChat selecting the vicuna template.