Instructions to use ubergarm/GLM-4.7-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 ubergarm/GLM-4.7-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 ubergarm/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

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 ubergarm/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

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 ubergarm/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/GLM-4.7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/GLM-4.7-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": "ubergarm/GLM-4.7-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K

Ollama
How to use ubergarm/GLM-4.7-GGUF with Ollama:
```
ollama run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/GLM-4.7-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 ubergarm/GLM-4.7-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 ubergarm/GLM-4.7-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use ubergarm/GLM-4.7-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GLM-4.7-GGUF:Q2_K

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": "ubergarm/GLM-4.7-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use ubergarm/GLM-4.7-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GLM-4.7-GGUF:Q2_K

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 "ubergarm/GLM-4.7-GGUF:Q2_K" \
  --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 ubergarm/GLM-4.7-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K
```

Lemonade

How to use ubergarm/GLM-4.7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/GLM-4.7-GGUF:Q2_K

Run and chat with the model

lemonade run user.GLM-4.7-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use ubergarm/GLM-4.7-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 ubergarm/GLM-4.7-GGUF:Q2_K

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 ubergarm/GLM-4.7-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

ubergarm commited on Dec 23, 2025

Commit

27f871a

1 Parent(s): 7af5d53

initial commit

Browse files

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----
-license: mit
----

+---
+quantized_by: ubergarm
+pipeline_tag: text-generation
+base_model: zai-org/GLM-4.7
+license: mit
+base_model_relation: quantized
+tags:
+- imatrix
+- conversational
+- ik_llama.cpp
+- glm4_moe
+language:
+  - en
+  - zh
+---
+## WIP
+Currently cooking this now!
+- [x] download bf16 safetensors https://huggingface.co/zai-org/GLM-4.7
+- [x] use llama.cpp/convert_hf_to_gguf.py to create bf16 GGUF
+- [ ] calculate imatrix and upload to HF first so others can use as desired
+- [ ] cook Q8_0 and test perplexity of BF16 and Q8_0 for baseline data
+- [ ] cook IQ5_K with full q8_0 attn/shexp/first 3 dense layers and test
+- [ ] upload IQ5_K if all looking good
+- [ ] continue with smaller quants
+- [ ] chek if any folks open discussions with desired RAM/VRAM breakpoints
+## `ik_llama.cpp` imatrix Quantizations of zai-org/GLM-4.7
+*NOTE* `ik_llama.cpp` can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.
+Some of ik's new quants are supported with [Nexesenex/croco.cpp](https://github.com/Nexesenex/croco.cpp) fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for [Windows builds by Thireus here.](https://github.com/Thireus/ik_llama.cpp/releases) which have been CUDA 12.8.
+These quants provide best in class perplexity for the given memory footprint.
+## Big Thanks
+Shout out to Wendell and the **Level1Techs** crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)!  **BIG thanks** for providing **BIG hardware** expertise and access to run these experiments and make these great quants available to the community!!!
+Also thanks to all the folks in the quanting and inferencing community on [BeaverAI Club Discord](https://huggingface.co/BeaverAI) and on [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) for tips and tricks helping each other run, test, and benchmark all the fun new models! Thanks to huggingface for hosting all these big quants!
+Finally, I *really* appreciate the support from [aifoundry.org](https://aifoundry.org) so check out their open source RISC-V based solutions!
+## Quant Collection
+Perplexity computed against *wiki.test.raw*.
+![Perplexity Chart](images/perplexity.png "Chart showing Perplexity improving as BPW increases.")
+These first two are just test quants for baseline perplexity comparison:
+* `BF16` 667.598 GiB (16.003 BPW)
+  - Final estimate: PPL = TODO
+* `Q8_0`
+  - Final estimate: PPL = TODO
+## IQ5_K TODO
+<details>
+<summary>👈 Secret Recipe</summary>
+```bash
+echo TODO
+```
+</details>
+## Quick Start
+```bash
+# Clone and checkout
+$ git clone https://github.com/ikawrakow/ik_llama.cpp
+$ cd ik_llama.cpp
+# Build for hybrid CPU+CUDA
+$ cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON
+$ cmake --build build --config Release -j $(nproc)
+# I'll follow up with updated commands especially for 2 to 4 CUDA GPUs for "graph parallel"
+# https://github.com/ikawrakow/ik_llama.cpp/pull/1080
+echo TODO
+```
+## References
+* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
+* [Getting Started Guide (already out of date lol)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)
+* [ubergarm-imatrix-calibration-corpus-v02.txt](https://gist.github.com/ubergarm/edfeb3ff9c6ec8b49e88cdf627b0711a?permalink_comment_id=5682584#gistcomment-5682584)
+* [Solid mainline quants by AesSedai/GLM-4.7-GGUF](https://huggingface.co/AesSedai/GLM-4.7-GGUF)