#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Paper • 2308.07074 • Published
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "OFA-Sys/InsTagger" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "OFA-Sys/InsTagger",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
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.
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "OFA-Sys/InsTagger" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OFA-Sys/InsTagger", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'