Instructions to use CMU-AIR2/code-ctrl-gh-mixture-240419 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CMU-AIR2/code-ctrl-gh-mixture-240419 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CMU-AIR2/code-ctrl-gh-mixture-240419")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CMU-AIR2/code-ctrl-gh-mixture-240419") model = AutoModelForCausalLM.from_pretrained("CMU-AIR2/code-ctrl-gh-mixture-240419", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CMU-AIR2/code-ctrl-gh-mixture-240419 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CMU-AIR2/code-ctrl-gh-mixture-240419" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CMU-AIR2/code-ctrl-gh-mixture-240419", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CMU-AIR2/code-ctrl-gh-mixture-240419
- SGLang
How to use CMU-AIR2/code-ctrl-gh-mixture-240419 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 "CMU-AIR2/code-ctrl-gh-mixture-240419" \ --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": "CMU-AIR2/code-ctrl-gh-mixture-240419", "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 "CMU-AIR2/code-ctrl-gh-mixture-240419" \ --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": "CMU-AIR2/code-ctrl-gh-mixture-240419", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CMU-AIR2/code-ctrl-gh-mixture-240419 with Docker Model Runner:
docker model run hf.co/CMU-AIR2/code-ctrl-gh-mixture-240419
Download scheduler.pt from CMU-AIR2/code-ctrl-gh-mixture-240419: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/CMU-AIR2/code-ctrl-gh-mixture-240419/resolve/main/scheduler.pt
- Command line
-
hf download hf://CMU-AIR2/code-ctrl-gh-mixture-240419/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/CMU-AIR2/code-ctrl-gh-mixture-240419/resolve/main/scheduler.pt
1.06 kB
- Xet hash:
- 006b3437c1f7a8238240b0ef3d1d2a3e4b307b9a8a65943d55c78c0154b3df45
- Size of remote file:
- 1.06 kB
- SHA256:
- 2be67f6aac8e482bb2022409709d8774ffb125292c0c9cf025c0ae747f3a6d57
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