Instructions to use CSgaoshouGroup/CSCupcakeCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CSgaoshouGroup/CSCupcakeCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CSgaoshouGroup/CSCupcakeCoder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CSgaoshouGroup/CSCupcakeCoder") model = AutoModelForCausalLM.from_pretrained("CSgaoshouGroup/CSCupcakeCoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CSgaoshouGroup/CSCupcakeCoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CSgaoshouGroup/CSCupcakeCoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CSgaoshouGroup/CSCupcakeCoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CSgaoshouGroup/CSCupcakeCoder
- SGLang
How to use CSgaoshouGroup/CSCupcakeCoder 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 "CSgaoshouGroup/CSCupcakeCoder" \ --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": "CSgaoshouGroup/CSCupcakeCoder", "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 "CSgaoshouGroup/CSCupcakeCoder" \ --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": "CSgaoshouGroup/CSCupcakeCoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CSgaoshouGroup/CSCupcakeCoder with Docker Model Runner:
docker model run hf.co/CSgaoshouGroup/CSCupcakeCoder
Download model-00002-of-00003.safetensors from CSgaoshouGroup/CSCupcakeCoder: direct link, hf CLI and curl.
- Browser
- Download file 4.99 GB
-
https://huggingface.co/CSgaoshouGroup/CSCupcakeCoder/resolve/main/model-00002-of-00003.safetensors
- Command line
-
hf download hf://CSgaoshouGroup/CSCupcakeCoder/model-00002-of-00003.safetensors
-
curl -L -o model-00002-of-00003.safetensors https://huggingface.co/CSgaoshouGroup/CSCupcakeCoder/resolve/main/model-00002-of-00003.safetensors
4.99 GB
- Xet hash:
- 0e5b0a6b2bab632988398195c73834b7550d44305fa626d5ef255b40ae9c4771
- Size of remote file:
- 4.99 GB
- SHA256:
- 2ec666dabfe7f264ded71960d576757367fd5e744e4df89d02c6ada8c7c5832f
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