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Model: OFA-Sys/InsTagger
Source: Original Platform

README.md

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+---
+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.