How to use from
SGLangUse 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 "GoCodeo/TestCodeo" \
--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": "GoCodeo/TestCodeo",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
TestCodeo - GoCodeo's fine-tuned Language Model dedicated to Python unit test generation.
Approach
Our team curated a unique dataset of 200,000 prompt-completion pairs in alpaca format, specifically designed for Python unit test generation.
Two-Stage Finetuning Process
Stage 1: We fine-tuned the base Codellama 7B Python model with 25k easy and 75k medium instructions.
Stage 2: The resulting Test-Codeo-Base was further refined with the remaining medium-hard questions to develop TestCodeo.
Evaluation Methodology
Utilizing OpenAI's human eval dataset, we generated test cases for 164 coding instructions and measured code coverage.
Results
TestCodeo achieved an impressive 89% code coverage, surpassing Codellama's 17% and approaching GPT-3.5-turbo's 93%.
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "GoCodeo/TestCodeo" \ --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": "GoCodeo/TestCodeo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'