Instructions to use cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF", filename="GLM-4.7-Flash-REAP-23B-A3B-BF16-to-ROCmFP4-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 cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
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 cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
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 cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
Use Docker
docker model run hf.co/cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
- LM Studio
- Jan
- Ollama
How to use cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF with Ollama:
ollama run hf.co/cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
- Unsloth Studio
How to use cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-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 cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-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 cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF to start chatting
- Pi
How to use cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
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": "cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
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 cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
Run Hermes
hermes
- Docker Model Runner
How to use cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF with Docker Model Runner:
docker model run hf.co/cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
- Lemonade
How to use cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF:BF16
Run and chat with the model
lemonade run user.GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF-BF16
List all available models
lemonade list
GLM 4.7 Flash REAP 23B-A3B ROCmFP4 GGUF
Unofficial GGUF quantization of GLM 4.7 Flash REAP 23B-A3B for local llama.cpp testing on AMD/Strix systems.
File
| File | Size |
|---|---|
GLM-4.7-Flash-REAP-23B-A3B-BF16-to-ROCmFP4-STRIX_LEAN.gguf |
12,292,302,592 bytes, about 11.45 GiB |
Filesystem display: about 12G.
Quantization
- Source precision: BF16 GGUF input
- Output format:
Q4_0_ROCMFP4 - Variant:
STRIX_LEAN - Runtime target: llama.cpp ROCmFP4 build with Vulkan or HIP/ROCm backend
Local Test Results
Quick local results on the maintainer's Strix setup:
| Backend / mode | Result |
|---|---|
| Vulkan, tg8 | ~73.28 tok/s |
| Vulkan, tg128 | ~50.16 tok/s |
| Vulkan, tg256 | ~48.30 tok/s |
HIP + GGML_HIP_ENABLE_UNIFIED_MEMORY=1, tg8 |
~59.39 tok/s |
HIP + GGML_HIP_ENABLE_UNIFIED_MEMORY=1, tg128 |
~41.46 tok/s |
HIP + GGML_HIP_ENABLE_UNIFIED_MEMORY=1, tg256 |
~40.09 tok/s |
| Vulkan Wikitext-2 quick PPL, ctx 2048, chunks 8 | ~15.4046 +/- 0.54576 |
| HIP Wikitext-2 quick PPL, ctx 2048, chunks 8 | ~14.7812 +/- 0.51969 |
Vulkan was preferred for interactive chat speed in local testing. HIP required unified memory on this setup.
llama.cpp Chat Example
Continuous Vulkan chat:
llama-cli \
-m GLM-4.7-Flash-REAP-23B-A3B-BF16-to-ROCmFP4-STRIX_LEAN.gguf \
-cnv \
-ngl 99 \
-fa on \
--no-mmap \
--jinja \
--reasoning off \
-c 32768 \
-b 512 \
-ub 512 \
-ctk q4_0 \
-ctv q4_0
Do not use -mli for normal Enter-to-send chat unless you intentionally want multiline input behavior.
HIP/ROCm example:
GGML_HIP_ENABLE_UNIFIED_MEMORY=1 llama-cli \
-m GLM-4.7-Flash-REAP-23B-A3B-BF16-to-ROCmFP4-STRIX_LEAN.gguf \
-cnv \
-ngl 99 \
-fa on \
--no-mmap \
--jinja \
--reasoning off \
-c 32768 \
-b 512 \
-ub 512 \
-ctk q4_0 \
-ctv q4_0
For coherence checks, compare -ctk q8_0 -ctv q8_0 against q4_0.
Notes
This is an experimental local-inference quantization. Confirm that your use complies with the upstream GLM model license and any applicable terms.
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Model tree for cafonez/GLM-4.7-Flash-REAP-23B-A3B-ROCmFP4-GGUF
Base model
zai-org/GLM-4.7-Flash