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.
InsTagger huggingface.co is an AI model on huggingface.co that provides InsTagger's model effect (), which can be used instantly with this OFA-Sys InsTagger model. huggingface.co supports a free trial of the InsTagger model, and also provides paid use of the InsTagger. Support call InsTagger model through api, including Node.js, Python, http.
InsTagger huggingface.co is an online trial and call api platform, which integrates InsTagger's modeling effects, including api services, and provides a free online trial of InsTagger, you can try InsTagger online for free by clicking the link below.
OFA-Sys InsTagger online free url in huggingface.co:
InsTagger is an open source model from GitHub that offers a free installation service, and any user can find InsTagger on GitHub to install. At the same time, huggingface.co provides the effect of InsTagger install, users can directly use InsTagger installed effect in huggingface.co for debugging and trial. It also supports api for free installation.