Instructions to use mradermacher/GLM-4.7-REAP-218B-A32B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/GLM-4.7-REAP-218B-A32B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/GLM-4.7-REAP-218B-A32B-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/GLM-4.7-REAP-218B-A32B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/GLM-4.7-REAP-218B-A32B-GGUF",
	filename="GLM-4.7-REAP-218B-A32B.IQ4_XS.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 mradermacher/GLM-4.7-REAP-218B-A32B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/GLM-4.7-REAP-218B-A32B-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 mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/GLM-4.7-REAP-218B-A32B-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 mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/GLM-4.7-REAP-218B-A32B-GGUF with Ollama:
```
ollama run hf.co/mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/GLM-4.7-REAP-218B-A32B-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 mradermacher/GLM-4.7-REAP-218B-A32B-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 mradermacher/GLM-4.7-REAP-218B-A32B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/GLM-4.7-REAP-218B-A32B-GGUF to start chatting

How to use mradermacher/GLM-4.7-REAP-218B-A32B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M

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": "mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mradermacher/GLM-4.7-REAP-218B-A32B-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 mradermacher/GLM-4.7-REAP-218B-A32B-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 mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use mradermacher/GLM-4.7-REAP-218B-A32B-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/GLM-4.7-REAP-218B-A32B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/GLM-4.7-REAP-218B-A32B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-4.7-REAP-218B-A32B-GGUF-Q4_K_M

List all available models

lemonade list

GLM-4.7-REAP-218B-A32B-GGUF / README.md

mradermacher

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metadata

base_model: cerebras/GLM-4.7-REAP-218B-A32B
language:
  - en
library_name: transformers
license: mit
license_link: https://huggingface.co/zai-org/GLM-4.7/blob/main/LICENSE
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
  - glm
  - MOE
  - pruning
  - compression

About

static quants of https://huggingface.co/cerebras/GLM-4.7-REAP-218B-A32B

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/GLM-4.7-REAP-218B-A32B-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	79.9
GGUF	Q3_K_S	94.4
GGUF	Q3_K_M	104.5	lower quality
GGUF	Q3_K_L	113.6
GGUF	IQ4_XS	117.6
GGUF	Q4_K_S	124.1	fast, recommended
GGUF	Q4_K_M	131.9	fast, recommended
GGUF	Q5_K_S	150.5
GGUF	Q5_K_M	154.9
GGUF	Q6_K	179.4	very good quality
P1 P2 P3 P4 P5	Q8_0	232.3	fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.