Instructions to use schuler/experimental-JP47G03C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schuler/experimental-JP47G03C with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="schuler/experimental-JP47G03C", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("schuler/experimental-JP47G03C", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use schuler/experimental-JP47G03C with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "schuler/experimental-JP47G03C" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "schuler/experimental-JP47G03C", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/schuler/experimental-JP47G03C
- SGLang
How to use schuler/experimental-JP47G03C 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 "schuler/experimental-JP47G03C" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "schuler/experimental-JP47G03C", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "schuler/experimental-JP47G03C" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "schuler/experimental-JP47G03C", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use schuler/experimental-JP47G03C with Docker Model Runner:
docker model run hf.co/schuler/experimental-JP47G03C
Commit History
-
Upload KPhi3ForCausalLM
Verified signature1b935eb
-
Upload KPhi3ForCausalLM
Verified signature3d02de0 -
Update README.md
Verified signaturec5bece2 -
Update tokenizer_config.json
Verified signaturecc07154 -
Update config.json
Verified signaturec2c2fd2 -
Upload 8 files
Verified signature1526dc7 -
Upload KPhi3ForCausalLM
Verified signaturef64ba07 -
initial commit
Verified signatured1455ee