How to use from
SGLang
# Gated model: Login with a HF token with gated access permission
hf auth login
Install from pip and serve model
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dnotitia/DNA-2.0-32B" \
    --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": "dnotitia/DNA-2.0-32B",
		"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 "dnotitia/DNA-2.0-32B" \
        --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": "dnotitia/DNA-2.0-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
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DNA 2.0

We introduce DNA 2.0, a latest advanced model that builds upon our innovative Smoothie Qwen3 model with enhanced various capabilities. By reducing Chinese token emission probabilities in the base Qwen3 model and applying uncensored training methodology inspired by Perplexity R1 1776, we've created a model that excels in analytical reasoning while maintaining genuinely open conversational capabilities.

This is a PRIVATE MODEL and is not available for public download. If you're looking for a public version, please check out DNA 2.0 14B instead.

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