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 "HenryTbelt/e2b-biocoder" \
--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": "HenryTbelt/e2b-biocoder",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}''pipeline_tag = "text-input"'
'task = "Code an autonomous environment simulator that incorporates machine learning for both the organisms and the environment itself. The environment should evolve over time, introducing new biological functions and rules dynamically. The simulation should utilize pixel graphics for visualization. The task is to develop a self-evolving environment that adapts and evolves through machine learning. You have complete creative freedom in implementing this project. Please ensure that the simulation is capable of updating itself while running. This includes the ability to introduce new features, functions, and rules to both the organisms and the environment. Please note that I am requesting this simulation to be visually represented using pixel graphics. You can start by referring to the instructions inside the file "C:\Users\henry\OneDrive\Desktop\abyagi.txt" for additional guidance or inspiration. Feel free to take creative liberties to achieve the desired outcome. (code a environment sim that uses machine learning for both the organisms and the environment itself so it can add more and moree biological functions over time as it runs)"
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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 "HenryTbelt/e2b-biocoder" \ --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": "HenryTbelt/e2b-biocoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'