Instructions to use TULLUS/codeparrot-small-multi-F32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TULLUS/codeparrot-small-multi-F32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TULLUS/codeparrot-small-multi-F32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TULLUS/codeparrot-small-multi-F32") model = AutoModelForCausalLM.from_pretrained("TULLUS/codeparrot-small-multi-F32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TULLUS/codeparrot-small-multi-F32 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TULLUS/codeparrot-small-multi-F32" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TULLUS/codeparrot-small-multi-F32", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TULLUS/codeparrot-small-multi-F32
- SGLang
How to use TULLUS/codeparrot-small-multi-F32 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 "TULLUS/codeparrot-small-multi-F32" \ --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": "TULLUS/codeparrot-small-multi-F32", "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 "TULLUS/codeparrot-small-multi-F32" \ --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": "TULLUS/codeparrot-small-multi-F32", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TULLUS/codeparrot-small-multi-F32 with Docker Model Runner:
docker model run hf.co/TULLUS/codeparrot-small-multi-F32
Commit History
Yesterday
-
Wrapped the ## markdown test results above
Verified signaturea9a7ee4 -
added [Full-Precision-F32] to repo link
Verified signature1152ea0 -
Update README.md
Verified signature8bb2a26 -
Update README.md
Verified signaturee0a28e8 -
Update README.md
Verified signature664e289 -
Update README.md
Verified signature0800088 -
Update README.md
Verified signature3348642
2 days ago
-
Update README.md
Verified signatured6cc476 -
Update README.md
Verified signaturecf005bc -
Update README.md
Verified signaturef6fd85f -
Update README.md
Verified signature68e252b
16 days ago
-
Update README.md
Verified signature2ef52a8 -
Update README.md
Verified signatureaa0a7f3 -
Create README.md
Verified signaturef0ad420 -
Add clean CodeParrot safetensors conversion
Verified signaturea06efe7 -
initial commit
Verified signatureda76730