Instructions to use sokann/GLM-5.2-GGUF-2.244bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sokann/GLM-5.2-GGUF-2.244bpw 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-2.244bpw
# Run inference directly in the terminal:
llama cli -hf sokann/GLM-5.2-GGUF-2.244bpw

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-2.244bpw
# Run inference directly in the terminal:
llama cli -hf sokann/GLM-5.2-GGUF-2.244bpw

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-2.244bpw
# Run inference directly in the terminal:
./llama-cli -hf sokann/GLM-5.2-GGUF-2.244bpw

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-2.244bpw
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sokann/GLM-5.2-GGUF-2.244bpw

Use Docker

docker model run hf.co/sokann/GLM-5.2-GGUF-2.244bpw

LM Studio
Jan
Ollama
How to use sokann/GLM-5.2-GGUF-2.244bpw with Ollama:
```
ollama run hf.co/sokann/GLM-5.2-GGUF-2.244bpw
```
Unsloth Desktop

How to use sokann/GLM-5.2-GGUF-2.244bpw 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-2.244bpw

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": "sokann/GLM-5.2-GGUF-2.244bpw"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sokann/GLM-5.2-GGUF-2.244bpw with Docker Model Runner:
```
docker model run hf.co/sokann/GLM-5.2-GGUF-2.244bpw
```

Lemonade

How to use sokann/GLM-5.2-GGUF-2.244bpw with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sokann/GLM-5.2-GGUF-2.244bpw

Run and chat with the model

lemonade run user.GLM-5.2-GGUF-2.244bpw-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use sokann/GLM-5.2-GGUF-2.244bpw 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-2.244bpw

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-2.244bpw

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sokann/GLM-5.2-GGUF-2.244bpw 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-2.244bpw

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-2.244bpw" \
  --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"

GLM-5.2-GGUF-2.244bpw

This is a 2.2 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_KT quant (2.125 BPW)
Indexer tensors use either the Q6_0 quant (6.5 BPW) or the Q8_0 quant (8.5 BPW)
All other tensors use the Q6_0 quant

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 3 versions:

GLM-5.2-GGUF-2.244bpw.gguf - Made with the imatrix from unsloth (thanks!)
GLM-5.2-GGUF-2.244bpw-q8indexer.gguf - Same as the above, but with Q8_0 for the indexer tensors
GLM-5.2-GGUF-2.244bpw-muzzy-imatrix.gguf - Same as the above, but with the imatrix from muzzy (thanks!)

Comparison:

version	imatrix	indexer	ppl
GLM-5.2-GGUF-2.244bpw.gguf	unsloth	Q6_0	3.8393 +/- 0.02167
GLM-5.2-GGUF-2.244bpw-q8indexer.gguf	unsloth	Q8_0	3.8393 +/- 0.02167
GLM-5.2-GGUF-2.244bpw-muzzy-imatrix.gguf	muzzy	Q8_0	3.6907 +/- 0.02071

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

Recommendation: GLM-5.2-GGUF-2.244bpw-muzzy-imatrix.gguf - The imatrix from muzzy, which took him 5 days to generate, is just amazing.

From testing, all 3 versions work very well for agentic coding.

Size

Size from llama-server output:

llm_load_print_meta: model size       = 196.756 GiB (2.244 BPW)
llm_load_print_meta: repeating layers = 195.315 GiB (2.233 BPW, 751.427 B parameters)

Buffer size with -cmoe --no-mmap:

llm_load_tensors:        CPU buffer size = 185087.34 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_kt

# Indexer
blk\..*\.indexer\.proj\.weight=q6_0
blk\..*\.indexer\.attn_k\.weight=q6_0
blk\..*\.indexer\.attn_q_b\.weight=q6_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

Speed comparison for tasks that can really benefit from MTP:

without MTP:

prompt eval time =   22486.83 ms /  5188 tokens (    4.33 ms per token,   230.71 tokens per second)
       eval time =   54116.43 ms /   639 tokens (   84.69 ms per token,    11.81 tokens per second)
      total time =   76603.26 ms /  5827 tokens

with MTP:

prompt eval time =   23404.00 ms /  5188 tokens (    4.51 ms per token,   221.67 tokens per second)
       eval time =   35463.34 ms /   639 tokens (   55.50 ms per token,    18.02 tokens per second)
      total time =   58867.34 ms /  5827 tokens
draft acceptance rate = 0.98259 (  508 accepted /   517 generated)
statistics mtp: #calls(b,g,a) = 1 130 130, #gen drafts = 130, #acc drafts = 129, #gen tokens = 517, #acc tokens = 508, dur(b,g,a) = 0.001, 1239.635, 0.063 ms

Comparison with other quants

quant	size	ppl
GLM-5.2-GGUF-2.244bpw-muzzy-imatrix.gguf	196.756 GiB (2.244 BPW)	3.6907 +/- 0.02071
IQ2_KS from muzzy/GLM-5.2-GGUF	205.738 GiB (2.344 BPW)	3.7897 +/- 0.02148
UD-IQ1_M from unsloth/GLM-5.2-GGUF	212.792 GiB (2.425 BPW)	4.1970 +/- 0.02421
UD-IQ2_XXS from unsloth/GLM-5.2-GGUF	222.073 GiB (2.530 BPW)	3.7598 +/- 0.02111

The IQ2_KS from https://huggingface.co/muzzy/GLM-5.2-GGUF, using the same recipe as the smol-IQ2_KS from https://huggingface.co/ubergarm/GLM-5.1-GGUF, is 9 GiB larger, and has a slightly worse PPL. However, IQ2_KS can provide better PP and TG than IQ2_KT, especially on CPU. It is a trade-off between quality and speed.

On the other hand, the UD-IQ1_M from https://huggingface.co/unsloth/GLM-5.2-GGUF is 16 GiB larger, while having a much worse PPL. The UD-IQ2_XXS is 25 GiB larger, yet still having a slightly worse PPL. Unless one is stuck with the mainline llama.cpp, there is no good reason to use these unsloth quants.

Downloads last month: 536

GGUF

Model size

753B params

Architecture

glm-dsa

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for sokann/GLM-5.2-GGUF-2.244bpw

Base model

zai-org/GLM-5.2

Quantized

(145)

this model