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

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

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.

Downloads last month: 281

GGUF

Model size

23B params

Architecture

deepseek2

Hardware compatibility

16-bit

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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

Quantized

(81)

this model