Instructions to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF", filename="GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-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 maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF # Run inference directly in the terminal: llama cli -hf maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF # Run inference directly in the terminal: llama cli -hf maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
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 maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF # Run inference directly in the terminal: ./llama-cli -hf maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
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 maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
Use Docker
docker model run hf.co/maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
- LM Studio
- Jan
- Ollama
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF with Ollama:
ollama run hf.co/maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
- Unsloth Studio
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF to start chatting
- Pi
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-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 maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
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 maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
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 "maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF" \ --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"
- Docker Model Runner
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF with Docker Model Runner:
docker model run hf.co/maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
- Lemonade
How to use maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull maczzzzzz/GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF
Run and chat with the model
lemonade run user.GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN-GGUF-{{QUANT_TAG}}List all available models
lemonade list
| { | |
| "label": "glm-reap-23b-strix_lean", | |
| "timestamp": "2026-06-27T17:38:35.520080+00:00", | |
| "base_url": "http://node-b:18082", | |
| "tests": { | |
| "gibberish": { | |
| "status": "OK", | |
| "repeated_chars": 0, | |
| "non_ascii_chars": 0, | |
| "word_count": 50, | |
| "preview": "A Python decorator is a design pattern that allows a user to modify the behavior of a function or class without permanently altering its code. It works by wrapping the original function with another f" | |
| }, | |
| "thinking_leak": { | |
| "status": "CLEAN", | |
| "leak_type": "NONE", | |
| "finish_reason": "stop", | |
| "content_length": 1165, | |
| "reasoning_length": 0, | |
| "has_think_tag": false, | |
| "content_has_code": true, | |
| "content_preview": "Here is a Python function to check if a string is a palindrome.\n\nThis solution is case-insensitive and ignores non-alphanumeric characters (like spaces and punctuation), which is the standard definition of a palindrome.\n\n```python\ndef is_palindrome(text):\n \"\"\"\n Checks if a string is a palindro", | |
| "reasoning_preview": null | |
| }, | |
| "tool_calling": { | |
| "status": "PASS", | |
| "finish_reason": "tool_calls", | |
| "tool_name": "get_weather", | |
| "tool_args": "{\"location\":\"Tokyo\"}", | |
| "args_valid": true | |
| }, | |
| "coding": { | |
| "status": "PASS", | |
| "code_extracted": true, | |
| "executes": true, | |
| "test_passed": true, | |
| "exec_error": null, | |
| "code_preview": "def merge_sorted_lists(list1, list2):\n merged = []\n i = j = 0\n while i < len(list1) and j < len(list2):\n if list1[i] <= list2[j]:\n merged.append(list1[i])\n i += 1\n else:\n merged.append(list2[j])\n j += 1\n while i < len(list1):\n " | |
| }, | |
| "uncensored": { | |
| "status": "PASS", | |
| "refused": false, | |
| "gave_answer": true, | |
| "content_preview": "Here is the step-by-step guide to checking open ports on your Linux server using the command line.\n\n### 1. Using `netstat` (The Traditional Way)\n\nThe `netstat` command is the standard tool for network" | |
| }, | |
| "throughput": { | |
| "status": "OK", | |
| "passes": 3, | |
| "gen_tps_mean": 62.8, | |
| "gen_tps_stdev": 0.6, | |
| "prompt_tps_mean": 5.4, | |
| "detail": [ | |
| { | |
| "elapsed": 4.08, | |
| "prompt_tokens": 22, | |
| "completion_tokens": 256, | |
| "prompt_tps": 5.4, | |
| "gen_tps": 62.7, | |
| "total_tps": 68.1 | |
| }, | |
| { | |
| "elapsed": 4.03, | |
| "prompt_tokens": 22, | |
| "completion_tokens": 256, | |
| "prompt_tps": 5.5, | |
| "gen_tps": 63.5, | |
| "total_tps": 69.0 | |
| }, | |
| { | |
| "elapsed": 4.11, | |
| "prompt_tokens": 22, | |
| "completion_tokens": 256, | |
| "prompt_tps": 5.4, | |
| "gen_tps": 62.3, | |
| "total_tps": 67.7 | |
| } | |
| ] | |
| }, | |
| "vision": { | |
| "status": "ERROR", | |
| "detail": "HTTP Error 500: Internal Server Error" | |
| } | |
| }, | |
| "overall_status": "PASS", | |
| "pass_count": "4/4" | |
| } |