Text Generation
Transformers
Safetensors
llama
research
code
mathematics
reasoning
multilingual
long-context
custom_code
text-generation-inference
Instructions to use DeepXR/Helion-V2.5-Rnd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepXR/Helion-V2.5-Rnd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DeepXR/Helion-V2.5-Rnd", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DeepXR/Helion-V2.5-Rnd", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("DeepXR/Helion-V2.5-Rnd", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use DeepXR/Helion-V2.5-Rnd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DeepXR/Helion-V2.5-Rnd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DeepXR/Helion-V2.5-Rnd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DeepXR/Helion-V2.5-Rnd
- SGLang
How to use DeepXR/Helion-V2.5-Rnd 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-V2.5-Rnd" \ --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": "DeepXR/Helion-V2.5-Rnd", "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 "DeepXR/Helion-V2.5-Rnd" \ --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": "DeepXR/Helion-V2.5-Rnd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DeepXR/Helion-V2.5-Rnd with Docker Model Runner:
docker model run hf.co/DeepXR/Helion-V2.5-Rnd
| # Core ML Framework | |
| torch==2.2.0 | |
| torchvision==0.17.0 | |
| torchaudio==2.2.0 | |
| # Transformers and Model Serving | |
| transformers==4.40.0 | |
| tokenizers==0.15.2 | |
| sentencepiece==0.2.0 | |
| accelerate==0.28.0 | |
| safetensors==0.4.2 | |
| huggingface-hub==0.21.4 | |
| # High-Performance Inference | |
| vllm==0.3.3 | |
| ray[default]==2.10.0 | |
| # Quantization Support | |
| bitsandbytes==0.43.0 | |
| # Web Server | |
| fastapi==0.110.0 | |
| uvicorn[standard]==0.29.0 | |
| aiohttp==3.9.3 | |
| websockets==12.0 | |
| # Data Processing | |
| numpy==1.26.4 | |
| scipy==1.12.0 | |
| pandas==2.2.1 | |
| pyarrow==15.0.2 | |
| # Model Utilities | |
| pydantic==2.6.4 | |
| pyyaml==6.0.1 | |
| omegaconf==2.3.0 | |
| # Monitoring and Logging | |
| prometheus-client==0.20.0 | |
| gputil==1.4.0 | |
| psutil==5.9.8 | |
| py-cpuinfo==9.0.0 | |
| pynvml==11.5.0 | |
| # HTTP Clients | |
| requests==2.31.0 | |
| httpx==0.27.0 | |
| # Development Tools | |
| pytest==8.1.1 | |
| pytest-asyncio==0.23.6 | |
| black==24.3.0 | |
| flake8==7.0.0 | |
| mypy==1.9.0 | |
| # Optional: Advanced Features | |
| scikit-learn==1.4.1 | |
| matplotlib==3.8.3 | |
| seaborn==0.13.2 | |
| pillow==10.2.0 |