Instructions to use sokann/GLM-5.2-GGUF-2.788bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sokann/GLM-5.2-GGUF-2.788bpw with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sokann/GLM-5.2-GGUF-2.788bpw",
	filename="GLM-5.2-GGUF-2.788bpw-muzzy-imatrix.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sokann/GLM-5.2-GGUF-2.788bpw 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 sokann/GLM-5.2-GGUF-2.788bpw
# Run inference directly in the terminal:
llama cli -hf sokann/GLM-5.2-GGUF-2.788bpw

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sokann/GLM-5.2-GGUF-2.788bpw
# Run inference directly in the terminal:
llama cli -hf sokann/GLM-5.2-GGUF-2.788bpw

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 sokann/GLM-5.2-GGUF-2.788bpw
# Run inference directly in the terminal:
./llama-cli -hf sokann/GLM-5.2-GGUF-2.788bpw

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 sokann/GLM-5.2-GGUF-2.788bpw
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sokann/GLM-5.2-GGUF-2.788bpw

Use Docker

docker model run hf.co/sokann/GLM-5.2-GGUF-2.788bpw

LM Studio
Jan
Ollama
How to use sokann/GLM-5.2-GGUF-2.788bpw with Ollama:
```
ollama run hf.co/sokann/GLM-5.2-GGUF-2.788bpw
```

Unsloth Studio

How to use sokann/GLM-5.2-GGUF-2.788bpw 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 sokann/GLM-5.2-GGUF-2.788bpw 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 sokann/GLM-5.2-GGUF-2.788bpw to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sokann/GLM-5.2-GGUF-2.788bpw to start chatting

How to use sokann/GLM-5.2-GGUF-2.788bpw with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sokann/GLM-5.2-GGUF-2.788bpw

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": "sokann/GLM-5.2-GGUF-2.788bpw"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sokann/GLM-5.2-GGUF-2.788bpw with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sokann/GLM-5.2-GGUF-2.788bpw

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 sokann/GLM-5.2-GGUF-2.788bpw

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sokann/GLM-5.2-GGUF-2.788bpw with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sokann/GLM-5.2-GGUF-2.788bpw

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 "sokann/GLM-5.2-GGUF-2.788bpw" \
  --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 sokann/GLM-5.2-GGUF-2.788bpw with Docker Model Runner:
```
docker model run hf.co/sokann/GLM-5.2-GGUF-2.788bpw
```

Lemonade

How to use sokann/GLM-5.2-GGUF-2.788bpw with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sokann/GLM-5.2-GGUF-2.788bpw

Run and chat with the model

lemonade run user.GLM-5.2-GGUF-2.788bpw-{{QUANT_TAG}}

List all available models

lemonade list

GLM-5.2-GGUF-2.788bpw

This is a 2.8 BPW quantized model for the GPU riches with more combined RAM + VRAM than common sense.

The quant aims to achieve best-in-class performance, by relying on SOTA quants from ik_llama.cpp:

Routed experts tensors use the IQ2_KL quant (2.6875 BPW)
Indexer tensors use the Q8_0 quant (8.5 BPW)
All other tensors use the Q6_0 quant (6.5 BPW)

Coupled with the recent enhancements:

MTP support with e.g. --spec-type mtp:n_max=4,p_min=0.0 (#1890)
graph parallel support with -sm graph (#1821)
DSA support with -dsa -fidx (#2045, #2098, #2109, and many others)

it should run at decent speed as well, with very little slowdown at long context.

(Note: For now, -sm graph and quantize KV cache e.g. -ctk q8_0 do not work together with -dsa -fidx. As a fun exercise, you can ask this quant to get them to work.)

Versions

There are 2 versions:

GLM-5.2-GGUF-2.788bpw.gguf - Made with the imatrix from unsloth (thanks!)
GLM-5.2-GGUF-2.788bpw-muzzy-imatrix.gguf - Made with the imatrix from muzzy (thanks!)

Comparison:

version	imatrix	indexer	ppl
GLM-5.2-GGUF-2.788bpw.gguf	unsloth	Q8_0	3.2731 +/- 0.01793
GLM-5.2-GGUF-2.788bpw-muzzy-imatrix.gguf	muzzy	Q8_0	3.2172 +/- 0.01751

The PPL results were gathered using llama-perplexity with -f wiki.test.raw -c 512.

Recommendation: GLM-5.2-GGUF-2.788bpw-muzzy-imatrix.gguf - The imatrix from muzzy, which took him 5 days to generate, is just amazing.

Size

Size from llama-server output:

llm_load_print_meta: model size       = 244.483 GiB (2.788 BPW)
llm_load_print_meta: repeating layers = 243.043 GiB (2.778 BPW, 751.427 B parameters)

Buffer size with -cmoe --no-mmap:

llm_load_tensors:        CPU buffer size = 233312.34 MiB
llm_load_tensors:      CUDA0 buffer size = 13724.12 MiB

Recipe

# Attention
blk\..*\.attn_k_b\.weight=q6_0
blk\..*\.attn_v_b\.weight=q6_0

blk\..*\.attn_kv_a_mqa\.weight=q6_0
blk\..*\.attn_q_a\.weight=q6_0
blk\..*\.attn_q_b\.weight=q6_0
blk\..*\.attn_output\.weight=q6_0

# First 3 Dense Layers
blk\..*\.ffn_down\.weight=q6_0
blk\..*\.ffn_(gate|up)\.weight=q6_0

# Shared Expert Layers
blk\..*\.ffn_down_shexp\.weight=q6_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q6_0

# Routed Experts Layers
blk\..*\.ffn_(up|gate|down)_exps\.weight=iq2_kl

# Indexer
blk\..*\.indexer\.proj\.weight=q8_0
blk\..*\.indexer\.attn_k\.weight=q8_0
blk\..*\.indexer\.attn_q_b\.weight=q8_0

# NextN MTP Layer
blk\..*\.nextn\.embed_tokens\.weight=q6_0
blk\..*\.nextn\.shared_head_head\.weight=q6_0
blk\..*\.nextn\.eh_proj\.weight=q6_0

# Non-Repeating Layers
token_embd\.weight=q6_0
output\.weight=q6_0

Flags

Example that uses DSA with IndexShare:

--no-mmap -ngl 99 --fit \
-mla 1 -dsa -fidx \
-c 102400 \
-b 2048 -ub 2048 -wgt 1 \
-cram 0 -muge -cuda graphs=1 \
--jinja --parallel-tool-calls \
--chat-template-kwargs {"reasoning_effort": "high"} \
--spec-type mtp:n_max=4,p_min=0.5

Comparison with other quants

quant	size	ppl
GLM-5.2-GGUF-2.788bpw-muzzy-imatrix.gguf	244.483 GiB (2.788 BPW)	3.2172 +/- 0.01751
IQ2_KL from muzzy/GLM-5.2-GGUF	261.988 GiB (2.985 BPW)	3.1085 +/- 0.01690
UD-IQ3_XXS from unsloth/GLM-5.2-GGUF	262.334 GiB (2.989 BPW)	3.2952 +/- 0.01824

The IQ2_KL from https://huggingface.co/muzzy/GLM-5.2-GGUF, using the same recipe as the IQ2_KL from https://huggingface.co/ubergarm/GLM-5.1-GGUF, is about 17.5 GiB larger than this, and has a better PPL. Recommended if there is enough RAM + VRAM.

On the other hand, the UD-IQ3_XXS from https://huggingface.co/unsloth/GLM-5.2-GGUF is even larger, while having a worse PPL. Unless one is stuck with the mainline llama.cpp, there is no good reason to use this unsloth quants.

Downloads last month: 1,061

GGUF

Model size

753B params

Architecture

glm-dsa

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for sokann/GLM-5.2-GGUF-2.788bpw

Base model

zai-org/GLM-5.2

Quantized

(112)

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