Instructions to use yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm") model = AutoModelForCausalLM.from_pretrained("yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm
- SGLang
How to use yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm 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 "yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm" \ --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": "yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm", "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 "yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm" \ --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": "yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm with Docker Model Runner:
docker model run hf.co/yanghuattt/microsoft_CodeGPT-small-java_2_ft_clm
- Xet hash:
- 779fc93349f6a5516bf9df8b88b5be008e63302db6aa5ad9d493346d3b741f8c
- Size of remote file:
- 498 MB
- SHA256:
- 3bb9305fb3c16946197c37390a6e6b4d4289c6cb368e6e53947887c3f613df12
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