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

GLM-4.7-GGUF / README.md

ubergarm

upload smol-IQ2_KS

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	---
	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
	---

	## `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
	NOTE: I'm working on some more quant recipes in the IQ2_KS-ish size range today Dec 24th...

	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 over 565 chunks for n_ctx=512 = 3.9267 +/- 0.02423
	* `Q8_0` 354.794 GiB (8.505 BPW)
	- Final estimate: PPL over 565 chunks for n_ctx=512 = 3.9320 +/- 0.02428

	NOTE: The first split file is much smaller on purpose to only contain metadata, its fine!

	## IQ5_K 250.635 GiB (6.008 BPW)
	Final estimate: PPL over 565 chunks for n_ctx=512 = 3.9445 +/- 0.02439

	<details>

	<summary>👈 Secret Recipe</summary>

	```bash
	#!/usr/bin/env bash

	custom="
	# 93 Repeating Layers [0-92]

	# Attention
	blk\..\.attn_q.=q8_0
	blk\..\.attn_k.=q8_0
	blk\..\.attn_v.=q8_0
	blk\..\.attn_output.=q8_0

	# First 3 Dense Layers [0-2]
	blk\..*\.ffn_down\.weight=q8_0
	blk\..*\.ffn_(gate\|up)\.weight=q8_0

	# Shared Expert Layers [3-92]
	blk\..*\.ffn_down_shexp\.weight=q8_0
	blk\..*\.ffn_(gate\|up)_shexp\.weight=q8_0

	# Routed Experts Layers [3-92]
	blk\..*\.ffn_down_exps\.weight=iq6_k
	blk\..*\.ffn_(gate\|up)_exps\.weight=iq5_k

	# NextN MTP Layer [92]
	# Leave full q8_0 as supposedly better for MTP
	# (doesn't use RAM or VRAM otherwise so its fine)
	blk\..*\.nextn\.embed_tokens\.weight=q8_0
	blk\..*\.nextn\.shared_head_head\.weight=q8_0
	blk\..*\.nextn\.eh_proj\.weight=q8_0

	# Non-Repeating Layers
	token_embd\.weight=iq6_k
	output\.weight=iq6_k
	"

	custom=$(
	echo "$custom" \| grep -v '^#' \| \
	sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
	)

	numactl -N ${SOCKET} -m ${SOCKET} \
	./build/bin/llama-quantize \
	--custom-q "$custom" \
	--imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
	/mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
	/mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-IQ5_K.gguf \
	IQ5_K \
	128
	```

	</details>

	## IQ3_KS 155.219 GiB (3.721 BPW)
	Final estimate: PPL over 565 chunks for n_ctx=512 = 4.1330 +/- 0.02573

	<details>

	<summary>👈 Secret Recipe</summary>

	```bash
	#!/usr/bin/env bash

	custom="
	# 93 Repeating Layers [0-92]

	# Attention
	blk\..\.attn_q.=q8_0
	blk\..\.attn_k.=q8_0
	blk\..\.attn_v.=q8_0
	blk\..\.attn_output.=q8_0

	# First 3 Dense Layers [0-2]
	blk\..*\.ffn_down\.weight=q8_0
	blk\..*\.ffn_(gate\|up)\.weight=q8_0

	# Shared Expert Layers [3-92]
	blk\..*\.ffn_down_shexp\.weight=q8_0
	blk\..*\.ffn_(gate\|up)_shexp\.weight=q8_0

	# Routed Experts Layers [3-92]
	blk\..*\.ffn_down_exps\.weight=iq4_kss
	blk\..*\.ffn_(gate\|up)_exps\.weight=iq3_ks

	# NextN MTP Layer [92]
	blk\..*\.nextn\.embed_tokens\.weight=q8_0
	blk\..*\.nextn\.shared_head_head\.weight=q8_0
	blk\..*\.nextn\.eh_proj\.weight=q8_0

	# Non-Repeating Layers
	token_embd\.weight=iq4_k
	output\.weight=iq6_k
	"

	custom=$(
	echo "$custom" \| grep -v '^#' \| \
	sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
	)

	numactl -N ${SOCKET} -m ${SOCKET} \
	./build/bin/llama-quantize \
	--custom-q "$custom" \
	--imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
	/mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
	/mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-IQ3_KS.gguf \
	IQ3_KS \
	128
	```

	</details>

	## smol-IQ2_KS 99.237 GiB (2.379 BPW)
	Final estimate: PPL over 565 chunks for n_ctx=512 = 5.9716 +/- 0.04130

	<details>

	<summary>👈 Secret Recipe</summary>

	```bash
	#!/usr/bin/env bash

	custom="
	# 93 Repeating Layers [0-92]

	# Attention
	blk\.(0\|1\|2)\.attn_q.*=iq6_k
	blk\.(0\|1\|2)\.attn_k.*=q8_0
	blk\.(0\|1\|2)\.attn_v.*=q8_0
	blk\.(0\|1\|2)\.attn_output.*=iq6_k

	blk\..\.attn_q.=iq5_ks
	blk\..\.attn_k.=iq6_k
	blk\..\.attn_v.=iq6_k
	blk\..\.attn_output.=iq5_ks

	# First 3 Dense Layers [0-2]
	blk\..*\.ffn_down\.weight=iq5_ks
	blk\..*\.ffn_(gate\|up)\.weight=iq5_ks

	# Shared Expert Layers [3-92]
	blk\..*\.ffn_down_shexp\.weight=iq5_ks
	blk\..*\.ffn_(gate\|up)_shexp\.weight=iq5_ks

	# Routed Experts Layers [3-92]
	blk\..*\.ffn_down_exps\.weight=iq2_ks
	blk\..*\.ffn_(gate\|up)_exps\.weight=iq2_ks

	# NextN MTP Layer [92]
	blk\..*\.nextn\.embed_tokens\.weight=q8_0
	blk\..*\.nextn\.shared_head_head\.weight=q8_0
	blk\..*\.nextn\.eh_proj\.weight=q8_0

	# Non-Repeating Layers
	token_embd\.weight=iq4_k
	output\.weight=iq6_k
	"

	custom=$(
	echo "$custom" \| grep -v '^#' \| \
	sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
	)

	numactl -N ${SOCKET} -m ${SOCKET} \
	./build/bin/llama-quantize \
	--custom-q "$custom" \
	--imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
	/mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
	/mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-v12-smol-IQ2_KS.gguf \
	IQ2_KS \
	128
	```

	</details>


	## smol-IQ1_KT 82.442 GiB (1.976 BPW)
	Final estimate: PPL over 565 chunks for n_ctx=512 = 6.7720 +/- 0.04745

	only for the desperate!

	<details>

	<summary>👈 Secret Recipe</summary>

	```bash
	#!/usr/bin/env bash

	custom="
	# 93 Repeating Layers [0-92]

	# Attention
	blk\.(0\|1\|2)\.attn_q.*=q8_0
	blk\.(0\|1\|2)\.attn_k.*=q8_0
	blk\.(0\|1\|2)\.attn_v.*=q8_0
	blk\.(0\|1\|2)\.attn_output.*=q8_0

	blk\..\.attn_q.=iq5_ks
	blk\..\.attn_k.=q8_0
	blk\..\.attn_v.=q8_0
	blk\..\.attn_output.=iq5_ks

	# First 3 Dense Layers [0-2]
	blk\..*\.ffn_down\.weight=iq5_ks
	blk\..*\.ffn_(gate\|up)\.weight=iq5_ks

	# Shared Expert Layers [3-92]
	blk\..*\.ffn_down_shexp\.weight=iq5_ks
	blk\..*\.ffn_(gate\|up)_shexp\.weight=iq5_ks

	# Routed Experts Layers [3-92]
	blk\..*\.ffn_down_exps\.weight=iq1_kt
	blk\..*\.ffn_(gate\|up)_exps\.weight=iq1_kt

	# NextN MTP Layer [92]
	blk\..*\.nextn\.embed_tokens\.weight=q8_0
	blk\..*\.nextn\.shared_head_head\.weight=q8_0
	blk\..*\.nextn\.eh_proj\.weight=q8_0

	# Non-Repeating Layers
	token_embd\.weight=iq4_k
	output\.weight=iq6_k
	"

	custom=$(
	echo "$custom" \| grep -v '^#' \| \
	sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
	)

	numactl -N ${SOCKET} -m ${SOCKET} \
	./build/bin/llama-quantize \
	--custom-q "$custom" \
	--imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
	/mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
	/mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-smol-IQ1_KT.gguf \
	IQ1_KT \
	128
	```

	</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)

	# Hybrid CPU + 1 GPU
	./build/bin/llama-sweep-bench \
	--model "$model" \
	--alias ubergarm/GLM-4.7 \
	--ctx-size 65536 \
	-ger \
	--merge-qkv \
	-ngl 99 \
	--n-cpu-moe 72 \
	-ub 4096 -b 4096 \
	--threads 24 \
	--parallel 1 \
	--host 127.0.0.1 \
	--port 8080 \
	--no-mmap \
	--jinja

	# Hybrid CPU + 2 or more GPUs
	# using new "-sm graph" 'tensor parallel' feature!
	# https://github.com/ikawrakow/ik_llama.cpp/pull/1080
	./build/bin/llama-sweep-bench \
	--model "$model" \
	--alias ubergarm/GLM-4.7 \
	--ctx-size 65536 \
	-ger \
	-sm graph \
	-smgs \
	-mea 256 \
	-ngl 99 \
	--n-cpu-moe 72 \
	-ts 41,48 \
	-ub 4096 -b 4096 \
	--threads 24 \
	--parallel 1 \
	--host 127.0.0.1 \
	--port 8080 \
	--no-mmap \
	--jinja
	# --max-gpu=3 # 3 or 4 usually if >2 GPUs available

	# CPU Only
	SOCKET=0 numactl -N ${SOCKET} -m ${SOCKET} \
	./build/bin/llama-server \
	--model "$model"\
	--alias ubergarm/GLM-4.7 \
	--ctx-size 65536 \
	-ger \
	--merge-qkv \
	-ctk q8_0 -ctv q8_0 \
	-ub 4096 -b 4096 \
	--parallel 1 \
	--threads 96 \
	--threads-batch 128 \
	--numa numactl \
	--host 127.0.0.1 \
	--port 8080 \
	--no-mmap \
	--jinja
	```
	NOTE: For tool/agentic use you can bring your own template with `--chat-template-file myTemplate.jinja` and might need `--special` etc.

	## 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)