MiniCPM-SALA

# Clone repository
git clone -b minicpm_sala https://github.com/OpenBMB/sglang.git
cd sglang

# One-click installation (creates venv and compiles all dependencies)
bash install_minicpm_sala.sh

# Or specify PyPI mirror
bash install_minicpm_sala.sh https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

# Activate environment
source sglang_minicpm_sala_env/bin/activate

# Launch Inference Server (Replace MODEL_PATH with actual path)
MODEL_PATH=/path/to/your/MiniCPM-SALA

python3 -m sglang.launch_server \
    --model ${MODEL_PATH} \
    --trust-remote-code \
    --disable-radix-cache \
    --attention-backend minicpm_flashinfer \
    --chunked-prefill-size 8192 \
    --max-running-requests 32 \
    --skip-server-warmup \
    --port 31111 \
    --dense-as-sparse

# 0. Ensure uv is installed
pip install uv

# 1. Create venv
uv venv --python 3.12 sglang_minicpm_sala_env
source sglang_minicpm_sala_env/bin/activate

# 2. Install SGLang
uv pip install --upgrade pip setuptools wheel
uv pip install -e ./python[all]

# 3. Compile CUDA Extensions
# (Ensure dependencies are cloned to 3rdparty/)
cd 3rdparty/infllmv2_cuda_impl && python setup.py install && cd ../..
cd 3rdparty/sparse_kernel && python setup.py install && cd ../..

# 4. Install extra deps
uv pip install tilelang flash-linear-attention