by OFA-Sys · Agent Tool · ★ 285
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InsTag: A Tool for Data Analysis in LLM Supervised Fine-tuning We introduce a tool named InsTag for 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. 🤗 InsTagger Checkpoint • 👉 Online LocalTagger Demo • 📖 Paper 🤖️ TagLM-13B-v1.0 Checkpoint 🤖️ TagLM-13B-v2.0 Checkpoint What is InsTag? Foundation language models obtain the instruction-following ability through supervised fine-tuning (SFT). Diversity and complexity are considered critical factors of a successful SFT dataset, while their definitions remain obscure and lack quantitative analyses. In this work, we propose InsTag, an open-set fine-grained tagger, to tag samples within SF
| Stars | 285 |
| Forks | 8 |
| Category | Agent Tool |
| Quality Score | 33.75/100 |
| Open Issues | 9 |
| Last Updated | 2023-08-20 |
| Created | 2023-08-14 |
| Est. Tokens | ~158k |
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InsTag is InsTag: A Tool for Data Analysis in LLM Supervised Fine-tuning. It is categorized as a Agent Tool with 285 GitHub stars.
You can find installation instructions and usage details in the InsTag GitHub repository at github.com/OFA-Sys/InsTag. The project has 285 stars and 8 forks, indicating an active community.
The top alternatives to InsTag on Agent Skills Hub include spacy-llm, LLM-Finetuning-Toolkit, autollm. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.