How to use from
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 "TeichAI/Qwen3-4B-Thinking-2507-Gemini-3-Flash-VIBE" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeichAI/Qwen3-4B-Thinking-2507-Gemini-3-Flash-VIBE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
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 "TeichAI/Qwen3-4B-Thinking-2507-Gemini-3-Flash-VIBE" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeichAI/Qwen3-4B-Thinking-2507-Gemini-3-Flash-VIBE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Quick Links

Qwen3 4B Thinking x Gemini 3 Flash VIBE

This model was trained on 200 agentic coding examples generated by gemini 3 flash preview.

For more info on how and what the model was trained on, please view the dataset card


This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

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