Text Generation
Transformers
Safetensors
English
code
helion-osc
mathematics
reasoning
algorithm
causal-lm
conversational
bitsandbytes
Instructions to use DeepXR/Helion-OSC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepXR/Helion-OSC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DeepXR/Helion-OSC") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DeepXR/Helion-OSC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DeepXR/Helion-OSC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DeepXR/Helion-OSC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DeepXR/Helion-OSC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DeepXR/Helion-OSC
- SGLang
How to use DeepXR/Helion-OSC 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 "DeepXR/Helion-OSC" \ --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": "DeepXR/Helion-OSC", "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 "DeepXR/Helion-OSC" \ --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": "DeepXR/Helion-OSC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DeepXR/Helion-OSC with Docker Model Runner:
docker model run hf.co/DeepXR/Helion-OSC
| # Python | |
| __pycache__/ | |
| *.py[cod] | |
| *$py.class | |
| *.so | |
| .Python | |
| build/ | |
| develop-eggs/ | |
| dist/ | |
| downloads/ | |
| eggs/ | |
| .eggs/ | |
| lib/ | |
| lib64/ | |
| parts/ | |
| sdist/ | |
| var/ | |
| wheels/ | |
| *.egg-info/ | |
| .installed.cfg | |
| *.egg | |
| MANIFEST | |
| # PyTorch | |
| *.pth | |
| *.pt | |
| *.ckpt | |
| # Virtual environments | |
| venv/ | |
| ENV/ | |
| env/ | |
| .venv | |
| # IDEs | |
| .vscode/ | |
| .idea/ | |
| *.swp | |
| *.swo | |
| *~ | |
| .DS_Store | |
| # Jupyter Notebook | |
| .ipynb_checkpoints | |
| *.ipynb | |
| # Model files (too large for git) | |
| *.safetensors | |
| *.bin | |
| *.h5 | |
| *.onnx | |
| # Logs | |
| logs/ | |
| *.log | |
| wandb/ | |
| tensorboard/ | |
| *.tfevents* | |
| # Evaluation results | |
| evaluation_results/ | |
| benchmark_results/ | |
| outputs/ | |
| # Cache | |
| .cache/ | |
| cache/ | |
| __pycache__/ | |
| *.pyc | |
| # Hugging Face | |
| .huggingface/ | |
| hf_cache/ | |
| # Offload | |
| offload/ | |
| # Testing | |
| .pytest_cache/ | |
| .coverage | |
| htmlcov/ | |
| # Temporary files | |
| tmp/ | |
| temp/ | |
| *.tmp | |
| # Compiled files | |
| *.o | |
| *.a | |
| *.lib | |
| *.dll | |
| *.exe | |
| # Documentation builds | |
| docs/_build/ | |
| site/ | |
| # Environment variables | |
| .env | |
| .env.local | |
| # Large files | |
| *.tar | |
| *.tar.gz | |
| *.zip | |
| *.7z | |
| # Safetensors shards (handled separately) | |
| # model-*.safetensors |