Instructions to use patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Use Docker
docker model run hf.co/patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF with Ollama:
ollama run hf.co/patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF with Docker Model Runner:
docker model run hf.co/patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
- Lemonade
How to use patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.GLM-5.3-Flash-REAP50-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "patrickbdevaney/GLM-5.3-Flash-REAP50-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Request: NVFP4 Version
GLM-5.3-Flash-REAP50-GGUF NVFP4 Version
Thanks!
GLM-5.3-Flash-REAP50-GGUF NVFP4 Version
Thanks!
https://huggingface.co/patrickbdevaney/GLM-5.3-Flash-REAP50-NVFP4-v2
That one is fp8.
How about a 4Bit NVFP4 version?
Or even better, Uncensored REAP50 MTP 4Bit NVFP4 version🐲🔥
That one is fp8.
How about a 4Bit NVFP4 version?
Or even better, REAP50 MTP 4Bit NVFP4 version🐲🔥
I thought that it's labeled wrong, and the weights compose the same file size, or maybe it was a wrong upload/quant. I want to iterate more on fine tuned draft heads- mtp and dflash for this model. And test with techniques for the fastest decode. Then look at further quantization or minimizing memory traffic per forward pass.
In any case, more is coming related to this model