Instructions to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF",
	filename="glm-4.7-flash-mxfp4_moe.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-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 FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

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 FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

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 FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

LM Studio
Jan

vLLM

How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

Ollama
How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE
```

Unsloth Studio

How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-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 FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-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 FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF to start chatting

How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

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": "FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-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 FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

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 FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

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 "FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE" \
  --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 FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE
```

Lemonade

How to use FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.GLM-4.7-Flash-MXFP4-MOE-GGUF-MXFP4_MOE

List all available models

lemonade list

GLM-4.7-Flash-MXFP4 MOE-GGUF

GGUF quantization of zai-org/GLM-4.7-Flash — a 30B-parameter Mixture-of-Experts language model with ~3.2B active parameters per token, built on the DeepSeek2 architecture with Multi-head Latent Attention (MLA) and 64 routed experts.

Quantized to MXFP4 MOE format for efficient inference with minimal quality loss.

About MXFP4 MOE

MXFP4 (Microscaling FP4, E2M1) is an open standard 4-bit format under the OCP Microscaling Formats (MX) specification. In MXFP4_MOE mode, expert weights are stored in MXFP4 while non-expert tensors (attention, embeddings, norms) remain at Q8_0, balancing quality and compression for Mixture-of-Experts models. Works on any GPU or CPU without hardware-specific acceleration.

Files

Filename	Type	Size	Description
`glm-4.7-flash-mxfp4_moe.gguf`	GGUF (MXFP4 MOE)	15.8 GB	Quantized model weights
`README.md`	Markdown	-	Model card

Quantization Details

Property	Value
Format	MXFP4 MOE
Bits Per Weight	4.53 BPW
File Size	15.8 GB
Tensor Count	844
Architecture	DeepSeek2 (custom for GLM-4.7-Flash)

Model Description

Developer: Zhipu AI
Architecture: Mixture-of-Experts (MoE) with DeepSeek2-style MLA
Parameters: ~30B total, ~3.2B active per token
Context Length: 200,000 tokens
Layers: 47 transformer layers
Attention: Multi-head Latent Attention (q_lora_rank=768, kv_lora_rank=512)
Experts: 64 routed experts (4 per token) + 1 shared expert
Vocab Size: 151,936
Languages: English, Chinese
Thinking: Enabled by default (native <think>/</think> tokens, hidden in history for clean multi-turn reasoning)
Pipeline: text-generation only (no vision encoder)

Usage

llama.cpp

# Basic generation
./llama-cli -m glm-4.7-flash-mxfp4_moe.gguf \
  -p "Hello, how are you?" \
  -n 256

# With thinking/reasoning controlled
./llama-cli -m glm-4.7-flash-mxfp4_moe.gguf \
  -p "Solve this step by step: 23 * 47" \
  -n 512 \
  -no-cnv

HuggingFace Hub

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF",
    filename="glm-4.7-flash-mxfp4_moe.gguf",
    repo_type="model"
)

Pipeline Commands

Source: zai-org/GLM-4.7-Flash (58 GB, 48 safetensor shards)

F16 GGUF Conversion:

python convert_hf_to_gguf.py D:\AI_MODELS\glm-4.7-src --outfile glm-4.7-f16.gguf --outtype f16

Output: 55.79 GB, 844 tensors (DeepSeek2 arch, Glm4MoeLiteModel)

MXFP4 MOE Quantization:

llama-quantize.exe glm-4.7-f16.gguf glm-4.7-flash-mxfp4_moe.gguf MXFP4_MOE

Duration: ~310s on RTX 5060 Ti

Hardware

Component	Specification
GPU	NVIDIA RTX 5060 Ti 16 GB (Blackwell)
System RAM	64 GB
Storage	D: (NVMe)

License

MIT — same as the original zai-org/GLM-4.7-Flash.

Downloads last month: 429

GGUF

Model size

30B params

Architecture

deepseek2

Hardware compatibility

4-bit

Model tree for FreedomAISVR/GLM-4.7-Flash-MXFP4-MOE-GGUF

Base model

zai-org/GLM-4.7-Flash

Quantized

(87)

this model