Instructions to use supreme-lab/ai-in-the-loop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use supreme-lab/ai-in-the-loop with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="supreme-lab/ai-in-the-loop")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("supreme-lab/ai-in-the-loop", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use supreme-lab/ai-in-the-loop with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "supreme-lab/ai-in-the-loop" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "supreme-lab/ai-in-the-loop", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/supreme-lab/ai-in-the-loop
- SGLang
How to use supreme-lab/ai-in-the-loop with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "supreme-lab/ai-in-the-loop" \ --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": "supreme-lab/ai-in-the-loop", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use 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 "supreme-lab/ai-in-the-loop" \ --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": "supreme-lab/ai-in-the-loop", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use supreme-lab/ai-in-the-loop with Docker Model Runner:
docker model run hf.co/supreme-lab/ai-in-the-loop
Download md-judge-multi-task/tokenizer_config.json from supreme-lab/ai-in-the-loop: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/supreme-lab/ai-in-the-loop/resolve/main/md-judge-multi-task/tokenizer_config.json
- Command line
-
hf download hf://supreme-lab/ai-in-the-loop/md-judge-multi-task/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/supreme-lab/ai-in-the-loop/resolve/main/md-judge-multi-task/tokenizer_config.json
1.03 kB
- Xet hash:
- 5eea35a81e3cc604e34518e92772e29d743c4eade0840c64e09f40f83273337f
- Size of remote file:
- 1.03 kB
- SHA256:
- c5052741b394891aa03f38b61fb141bd7ef264fefc70c189ec04b7a0a2964368
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.