28 lines
1.2 KiB
Markdown
28 lines
1.2 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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---
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# InsTagger
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**InsTagger** is an tool for automatically providing instruction tags by distilling tagging results from **InsTag**.
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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.
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### Model Description
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- **Model type:** Auto-regressive Models
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- **Language(s) (NLP):** English
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- **License:** apache-2.0
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- **Finetuned from model:** LLaMa-2
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### Model Sources [optional]
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- **Repository:** [https://github.com/OFA-Sys/InsTag](https://github.com/OFA-Sys/InsTag)
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- **Paper:** [Arxiv](https://arxiv.org/pdf/2308.07074.pdf)
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- **Demo:** [ModelScope Demo](https://www.modelscope.cn/studios/lukeminglkm/instagger_demo/summary)
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## Uses
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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. |