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

uploading IQ5_K

75dda6d 8 months ago

6.35 kB

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!

download bf16 safetensors https://huggingface.co/zai-org/GLM-4.7
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
adjust MTP nextn tensors to full q8_0 (won't effect RAM+VRAM usage otherwise)
cook IQ5_K with full q8_0 attn/shexp/first 3 dense layers and test
upload IQ5_K if all looking good
upload smol-IQ4_KSS if all looking good
continue with smaller quants
check 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 fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for Windows builds by Thireus here. 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, YouTube Channel! 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 and on 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 so check out their open source RISC-V based solutions!

Quant Collection

Perplexity computed against wiki.test.raw.

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

IQ5_K 250.635 GiB (6.008 BPW)

Final estimate: PPL over 565 chunks for n_ctx=512 = 3.9445 +/- 0.02439

👈 Secret Recipe

#!/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

smol-IQ4_KSS TODO

Final estimate: PPL over 565 chunks for n_ctx=512 = TODO

👈 Secret Recipe

#!/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=iq4_kss

# 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-IQ4_KSS.gguf \
    IQ4_KSS \
    128

Quick Start

# 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

ubergarm
/

GLM-4.7-GGUF

WIP

`ik_llama.cpp` imatrix Quantizations of zai-org/GLM-4.7

Big Thanks

Quant Collection

IQ5_K 250.635 GiB (6.008 BPW)

smol-IQ4_KSS TODO

Quick Start

References

WIP

ik_llama.cpp imatrix Quantizations of zai-org/GLM-4.7

Big Thanks

Quant Collection

IQ5_K 250.635 GiB (6.008 BPW)

smol-IQ4_KSS TODO

Quick Start

References

`ik_llama.cpp` imatrix Quantizations of zai-org/GLM-4.7