Instructions to use khasinski/GLM-4.7-Flash-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use khasinski/GLM-4.7-Flash-Q8_0-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 khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

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 khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

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 khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use khasinski/GLM-4.7-Flash-Q8_0-GGUF with Ollama:
```
ollama run hf.co/khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0
```

Unsloth Studio

How to use khasinski/GLM-4.7-Flash-Q8_0-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 khasinski/GLM-4.7-Flash-Q8_0-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 khasinski/GLM-4.7-Flash-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for khasinski/GLM-4.7-Flash-Q8_0-GGUF to start chatting

How to use khasinski/GLM-4.7-Flash-Q8_0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

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": "khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use khasinski/GLM-4.7-Flash-Q8_0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

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 "khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0" \
  --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 khasinski/GLM-4.7-Flash-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0
```

Lemonade

How to use khasinski/GLM-4.7-Flash-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.GLM-4.7-Flash-Q8_0-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use khasinski/GLM-4.7-Flash-Q8_0-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 khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

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 khasinski/GLM-4.7-Flash-Q8_0-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

GLM-4.7-Flash Q8_0 GGUF

Q8_0 quantization of zai-org/GLM-4.7-Flash for use with llama.cpp.

Model Details

Property	Value
Base model	zai-org/GLM-4.7-Flash
Architecture	30B-A3B MoE (DeepSeek v2)
Quantization	Q8_0
Size	~30 GB
Context length	128K tokens

Hardware Requirements

Minimum VRAM: 32 GB (single GPU)
Recommended: 56 GB (dual GPU, e.g., RTX 5090 + RTX 4090)

Usage

Basic usage with llama.cpp

llama-server -m GLM-4.7-Flash-Q8_0.gguf -ngl 99 -c 65536

Full 128K context on dual GPU

llama-server -m GLM-4.7-Flash-Q8_0.gguf \
    -ngl 99 \
    -c 131072 \
    --cache-type-k q8_0 \
    --cache-type-v q8_0 \
    --split-mode layer \
    --tensor-split 32,24 \
    --host 0.0.0.0 \
    --port 8080

OpenAI-compatible API

After starting the server, the API is available at:

http://localhost:8080/v1/chat/completions
http://localhost:8080/v1/completions

Quantization Notes

This model was quantized from ngxson/GLM-4.7-Flash-GGUF F16 version.

The missing deepseek2.rope.scaling.yarn_log_multiplier metadata key was added to enable quantization with llama.cpp.

Quality Comparison

Quantization	Size	Perplexity Impact
F16	56 GB	Baseline
Q8_0	30 GB	~0.1%
Q4_K_M	18 GB	~2-4%

Q8_0 provides near-lossless quality while reducing model size by ~47%.

Downloads last month: 38

GGUF

Model size

30B params

Architecture

deepseek2

Hardware compatibility

8-bit

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Model tree for khasinski/GLM-4.7-Flash-Q8_0-GGUF

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