Instructions to use ngxson/GLM-4.7-Flash-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 ngxson/GLM-4.7-Flash-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 ngxson/GLM-4.7-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ngxson/GLM-4.7-Flash-GGUF:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/ngxson/GLM-4.7-Flash-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ngxson/GLM-4.7-Flash-GGUF with Ollama:
```
ollama run hf.co/ngxson/GLM-4.7-Flash-GGUF:Q4_K_M
```
Unsloth Desktop

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ngxson/GLM-4.7-Flash-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ngxson/GLM-4.7-Flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ngxson/GLM-4.7-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/ngxson/GLM-4.7-Flash-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ngxson/GLM-4.7-Flash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-4.7-Flash-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ngxson/GLM-4.7-Flash-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 ngxson/GLM-4.7-Flash-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 ngxson/GLM-4.7-Flash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ngxson/GLM-4.7-Flash-GGUF:Q4_K_M

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 "ngxson/GLM-4.7-Flash-GGUF:Q4_K_M" \
  --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"

Deepseek architecture?

by spanspek - opened Jan 19

Discussion

spanspek

Jan 19

I don't think this model is DeepSeek architecture, as far as I can tell it's the same as GLM 4.7 which is still based on the architecture used since GLM 4.5

https://huggingface.co/zai-org/GLM-4.7-Flash?inference_provider=zai-org

The link to the technical document is to GLM 4.5

Does the model work after quantizing it as DeepSeek, is it producing coherent output?

toriset

Jan 19

ive had to do the same thing before quants came out, it produces coherent output but i had one single output which was incoherent (kept rambling without stop)

toriset

Jan 19

someone else posted and is having issues, might need llama.cpp native support

bezo97

Jan 19

Agree this conversion is broken (non-stop rambling).

Interestingly it notices that something is wrong with stuff like:

Correction: The input is extremely messy.

Tridefender

Jan 19

Not DeepSeek, but this is in order to borrow the MLA attention thing. Needs more support on lcpp side.

spanspek

Jan 20

Unsloth are also using the deepseek2 architecture...

https://ibb.co/YFgQjDg1

https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF

spanspek

Jan 20

Not DeepSeek, but this is in order to borrow the MLA attention thing. Needs more support on lcpp side.

In that case shouldn't we wait for llama.cpp to provide support for it and then GGUF them correctly? I mean using the deepseek architecture might get them working but will it get them working at full functionality / quality?

Tridefender

Jan 22

Not DeepSeek, but this is in order to borrow the MLA attention thing. Needs more support on lcpp side.

In that case shouldn't we wait for llama.cpp to provide support for it and then GGUF them correctly? I mean using the deepseek architecture might get them working but will it get them working at full functionality / quality?

From all we know FA is broken at that time

spanspek changed discussion status to closed Jan 23

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment