Instructions to use boimbukanbaim/codeparrot-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boimbukanbaim/codeparrot-ds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="boimbukanbaim/codeparrot-ds")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("boimbukanbaim/codeparrot-ds") model = AutoModelForCausalLM.from_pretrained("boimbukanbaim/codeparrot-ds") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use boimbukanbaim/codeparrot-ds with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "boimbukanbaim/codeparrot-ds" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "boimbukanbaim/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/boimbukanbaim/codeparrot-ds
- SGLang
How to use boimbukanbaim/codeparrot-ds 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 "boimbukanbaim/codeparrot-ds" \ --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": "boimbukanbaim/codeparrot-ds", "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 "boimbukanbaim/codeparrot-ds" \ --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": "boimbukanbaim/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use boimbukanbaim/codeparrot-ds with Docker Model Runner:
docker model run hf.co/boimbukanbaim/codeparrot-ds
- Xet hash:
- d0ee6b56b912abaf7f51cb075091ee8e3c6260d17f633ce13deb2174567f0700
- Size of remote file:
- 497 MB
- SHA256:
- a0c3c001d0823383918b554812481ba70fb5d8fb04ff7a74328d230b85048745
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