Instructions to use QuantTrio/GLM-5.1-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantTrio/GLM-5.1-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuantTrio/GLM-5.1-AWQ") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("QuantTrio/GLM-5.1-AWQ") model = AutoModelForCausalLM.from_pretrained("QuantTrio/GLM-5.1-AWQ") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use QuantTrio/GLM-5.1-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantTrio/GLM-5.1-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantTrio/GLM-5.1-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantTrio/GLM-5.1-AWQ
- SGLang
How to use QuantTrio/GLM-5.1-AWQ 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 "QuantTrio/GLM-5.1-AWQ" \ --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": "QuantTrio/GLM-5.1-AWQ", "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 "QuantTrio/GLM-5.1-AWQ" \ --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": "QuantTrio/GLM-5.1-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use QuantTrio/GLM-5.1-AWQ with Docker Model Runner:
docker model run hf.co/QuantTrio/GLM-5.1-AWQ
| # Script to build and/or install DeepGEMM from source | |
| # Default: build and install immediately | |
| # Optional: build wheels to a directory for later installation (useful in multi-stage builds) | |
| set -e | |
| # Default values | |
| DEEPGEMM_GIT_REPO="https://github.com/deepseek-ai/DeepGEMM.git" | |
| DEEPGEMM_GIT_REF="0f5f2662027f0db05d4e3f6a94e56e2d8fc45c51" | |
| WHEEL_DIR="" | |
| # Parse command line arguments | |
| while [[ $# -gt 0 ]]; do | |
| case $1 in | |
| --ref) | |
| if [[ -z "$2" || "$2" =~ ^- ]]; then | |
| echo "Error: --ref requires an argument." >&2 | |
| exit 1 | |
| fi | |
| DEEPGEMM_GIT_REF="$2" | |
| shift 2 | |
| ;; | |
| --cuda-version) | |
| if [[ -z "$2" || "$2" =~ ^- ]]; then | |
| echo "Error: --cuda-version requires an argument." >&2 | |
| exit 1 | |
| fi | |
| CUDA_VERSION="$2" | |
| shift 2 | |
| ;; | |
| --wheel-dir) | |
| if [[ -z "$2" || "$2" =~ ^- ]]; then | |
| echo "Error: --wheel-dir requires a directory path." >&2 | |
| exit 1 | |
| fi | |
| WHEEL_DIR="$2" | |
| shift 2 | |
| ;; | |
| -h|--help) | |
| echo "Usage: $0 [OPTIONS]" | |
| echo "Options:" | |
| echo " --ref REF Git reference to checkout (default: $DEEPGEMM_GIT_REF)" | |
| echo " --cuda-version VER CUDA version (auto-detected if not provided)" | |
| echo " --wheel-dir PATH If set, build wheel into PATH but do not install" | |
| echo " -h, --help Show this help message" | |
| exit 0 | |
| ;; | |
| *) | |
| echo "Unknown option: $1" >&2 | |
| exit 1 | |
| ;; | |
| esac | |
| done | |
| # Auto-detect CUDA version if not provided | |
| if [ -z "$CUDA_VERSION" ]; then | |
| if command -v nvcc >/dev/null 2>&1; then | |
| CUDA_VERSION=$(nvcc --version | grep "release" | sed -n 's/.*release \([0-9]\+\.[0-9]\+\).*/\1/p') | |
| echo "Auto-detected CUDA version: $CUDA_VERSION" | |
| else | |
| echo "Warning: Could not auto-detect CUDA version. Please specify with --cuda-version" | |
| exit 1 | |
| fi | |
| fi | |
| # Extract major and minor version numbers | |
| CUDA_MAJOR="${CUDA_VERSION%%.*}" | |
| CUDA_MINOR="${CUDA_VERSION#${CUDA_MAJOR}.}" | |
| CUDA_MINOR="${CUDA_MINOR%%.*}" | |
| echo "CUDA version: $CUDA_VERSION (major: $CUDA_MAJOR, minor: $CUDA_MINOR)" | |
| # Check CUDA version requirement | |
| if [ "$CUDA_MAJOR" -lt 12 ] || { [ "$CUDA_MAJOR" -eq 12 ] && [ "$CUDA_MINOR" -lt 8 ]; }; then | |
| echo "Skipping DeepGEMM build/installation (requires CUDA 12.8+ but got ${CUDA_VERSION})" | |
| exit 0 | |
| fi | |
| echo "Preparing DeepGEMM build..." | |
| echo "Repository: $DEEPGEMM_GIT_REPO" | |
| echo "Reference: $DEEPGEMM_GIT_REF" | |
| # Create a temporary directory for the build | |
| # INSTALL_DIR=$(mktemp -d) | |
| INSTALL_DIR="." | |
| trap 'rm -rf "$INSTALL_DIR"' EXIT | |
| # Clone the repository | |
| git clone --recursive --shallow-submodules "$DEEPGEMM_GIT_REPO" "$INSTALL_DIR/deepgemm" | |
| pushd "$INSTALL_DIR/deepgemm" | |
| # Checkout the specific reference | |
| git checkout "$DEEPGEMM_GIT_REF" | |
| # Clean previous build artifacts | |
| # (Based on https://github.com/deepseek-ai/DeepGEMM/blob/main/install.sh) | |
| rm -rf build dist *.egg-info | |
| # Build wheel | |
| echo "🏗️ Building DeepGEMM wheel..." | |
| python3 setup.py bdist_wheel | |
| # If --wheel-dir was specified, copy wheels there and exit | |
| if [ -n "$WHEEL_DIR" ]; then | |
| mkdir -p "$WHEEL_DIR" | |
| cp dist/*.whl "$WHEEL_DIR"/ | |
| echo "✅ Wheel built and copied to $WHEEL_DIR" | |
| popd | |
| exit 0 | |
| fi | |
| # Default behaviour: install built wheel | |
| if command -v uv >/dev/null 2>&1; then | |
| echo "Installing DeepGEMM wheel using uv..." | |
| if [ -n "$VLLM_DOCKER_BUILD_CONTEXT" ]; then | |
| uv pip install --system dist/*.whl | |
| else | |
| uv pip install dist/*.whl | |
| fi | |
| else | |
| echo "Installing DeepGEMM wheel using pip..." | |
| python3 -m pip install dist/*.whl | |
| fi | |
| popd | |
| echo "✅ DeepGEMM installation completed successfully" | |