Instructions to use Jordansky/testpush_sftgoy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jordansky/testpush_sftgoy with PEFT:
Base model is not found.
- Transformers
How to use Jordansky/testpush_sftgoy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jordansky/testpush_sftgoy") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jordansky/testpush_sftgoy", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Jordansky/testpush_sftgoy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jordansky/testpush_sftgoy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jordansky/testpush_sftgoy", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Jordansky/testpush_sftgoy
- SGLang
How to use Jordansky/testpush_sftgoy 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 "Jordansky/testpush_sftgoy" \ --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": "Jordansky/testpush_sftgoy", "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 "Jordansky/testpush_sftgoy" \ --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": "Jordansky/testpush_sftgoy", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Jordansky/testpush_sftgoy with Docker Model Runner:
docker model run hf.co/Jordansky/testpush_sftgoy
- Xet hash:
- 852225e8046b20326f3f10b3b61a594c73eb99739893c6a8b2eaf1156b128076
- Size of remote file:
- 8.08 kB
- SHA256:
- 63a8ed61f90ad84a525cf3085bb8c6745fcc7a1c519ffa4d959c37f277ea9ccf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.