Instructions to use Dawn-AI/Dawn-superlite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dawn-AI/Dawn-superlite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Dawn-AI/Dawn-superlite", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Dawn-AI/Dawn-superlite", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Dawn-AI/Dawn-superlite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Dawn-AI/Dawn-superlite" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dawn-AI/Dawn-superlite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Dawn-AI/Dawn-superlite
- SGLang
How to use Dawn-AI/Dawn-superlite 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 "Dawn-AI/Dawn-superlite" \ --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": "Dawn-AI/Dawn-superlite", "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 "Dawn-AI/Dawn-superlite" \ --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": "Dawn-AI/Dawn-superlite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Dawn-AI/Dawn-superlite with Docker Model Runner:
docker model run hf.co/Dawn-AI/Dawn-superlite
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13f910f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | #!/usr/bin/env bash
set -eo pipefail
CONDA_ROOT="/lustre/fsw/portfolios/nvr/projects/nvr_lpr_llm/users/yongganf/anaconda"
ENV_NAME="nemotron_flash"
PYTHON_VERSION="3.10.12"
source "${CONDA_ROOT}/etc/profile.d/conda.sh"
if conda env list | awk '{print $1}' | grep -qx "${ENV_NAME}"; then
echo "Conda env '${ENV_NAME}' already exists, skipping creation."
else
echo "Creating conda env '${ENV_NAME}' with Python ${PYTHON_VERSION}..."
conda create -n "${ENV_NAME}" "python=${PYTHON_VERSION}" -y
fi
conda activate "${ENV_NAME}"
echo "Installing CUDA toolkit 12.8..."
conda install -c nvidia cuda-toolkit=12.8 -y
export CUDA_HOME
CUDA_HOME="$(dirname "$(dirname "$(which nvcc)")")"
echo "CUDA_HOME set to ${CUDA_HOME}"
echo "Installing PyTorch and core Python packages..."
pip install torch==2.8.0 transformers==4.56.2 psutil
echo "Installing causal-conv1d from source..."
pip install --no-build-isolation causal-conv1d
echo "Installing flash-attention from source..."
pip install --no-build-isolation flash-attn==2.7.3
echo "Installing mamba-ssm from source..."
pip install --no-build-isolation mamba-ssm
echo "Installing flash-linear-attention..."
pip install --no-build-isolation flash-linear-attention
echo "Done! Environment '${ENV_NAME}' is ready."
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