Instructions to use WizardLMTeam/WizardCoder-Python-13B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WizardLMTeam/WizardCoder-Python-13B-V1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WizardLMTeam/WizardCoder-Python-13B-V1.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardCoder-Python-13B-V1.0") model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardCoder-Python-13B-V1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use WizardLMTeam/WizardCoder-Python-13B-V1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WizardLMTeam/WizardCoder-Python-13B-V1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WizardLMTeam/WizardCoder-Python-13B-V1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WizardLMTeam/WizardCoder-Python-13B-V1.0
- SGLang
How to use WizardLMTeam/WizardCoder-Python-13B-V1.0 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 "WizardLMTeam/WizardCoder-Python-13B-V1.0" \ --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": "WizardLMTeam/WizardCoder-Python-13B-V1.0", "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 "WizardLMTeam/WizardCoder-Python-13B-V1.0" \ --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": "WizardLMTeam/WizardCoder-Python-13B-V1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WizardLMTeam/WizardCoder-Python-13B-V1.0 with Docker Model Runner:
docker model run hf.co/WizardLMTeam/WizardCoder-Python-13B-V1.0
Commit History
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Update README.md (#3)
- Update README.md (13a38464953b3b6b8b34778e5bf1215c9d79ef75) Co-authored-by: Ziyang Luo <Ziyang@users.noreply.huggingface.co>4000a99
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Update README.md (#2)
- Update README.md (c2287b38197cf3f53afb3a7b90115bffdddcc000) Co-authored-by: Ziyang Luo <Ziyang@users.noreply.huggingface.co>8db434a -
Update README.md (#1)
- Update README.md (5de732f03847d2be4b05507015f6547058187b75) Co-authored-by: haipengluo <haipeng1@users.noreply.huggingface.co>95a7439 -
initial release
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Update README.md
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initial commit
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