Instructions to use 5handilya/qwen2.5coder7b-svg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 5handilya/qwen2.5coder7b-svg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="5handilya/qwen2.5coder7b-svg")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("5handilya/qwen2.5coder7b-svg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 5handilya/qwen2.5coder7b-svg with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "5handilya/qwen2.5coder7b-svg" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "5handilya/qwen2.5coder7b-svg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/5handilya/qwen2.5coder7b-svg
- SGLang
How to use 5handilya/qwen2.5coder7b-svg 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 "5handilya/qwen2.5coder7b-svg" \ --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": "5handilya/qwen2.5coder7b-svg", "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 "5handilya/qwen2.5coder7b-svg" \ --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": "5handilya/qwen2.5coder7b-svg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 5handilya/qwen2.5coder7b-svg with Docker Model Runner:
docker model run hf.co/5handilya/qwen2.5coder7b-svg
Download config.json from 5handilya/qwen2.5coder7b-svg: direct link, hf CLI and curl.
- Browser
- Download file 737 Bytes
-
https://huggingface.co/5handilya/qwen2.5coder7b-svg/resolve/main/config.json
- Command line
-
hf download hf://5handilya/qwen2.5coder7b-svg/config.json
-
curl -L -o config.json https://huggingface.co/5handilya/qwen2.5coder7b-svg/resolve/main/config.json
737 Bytes
- Xet hash:
- 90e09c1f3930c7c09c51572737b59a8881829a8b1d43edf54ae2dc44a67687e3
- Size of remote file:
- 737 Bytes
- SHA256:
- 268fcebf8a9c8ff2ee06de88ef4e33d0b586922cff4047f56047b60328912c54
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.