Instructions to use meshllm/GLM-5.1-Q3_K_M-plus-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meshllm/GLM-5.1-Q3_K_M-plus-layers with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="meshllm/GLM-5.1-Q3_K_M-plus-layers",
	filename="layers/layer-000.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use meshllm/GLM-5.1-Q3_K_M-plus-layers 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 meshllm/GLM-5.1-Q3_K_M-plus-layers
# Run inference directly in the terminal:
llama cli -hf meshllm/GLM-5.1-Q3_K_M-plus-layers

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf meshllm/GLM-5.1-Q3_K_M-plus-layers
# Run inference directly in the terminal:
llama cli -hf meshllm/GLM-5.1-Q3_K_M-plus-layers

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 meshllm/GLM-5.1-Q3_K_M-plus-layers
# Run inference directly in the terminal:
./llama-cli -hf meshllm/GLM-5.1-Q3_K_M-plus-layers

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 meshllm/GLM-5.1-Q3_K_M-plus-layers
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meshllm/GLM-5.1-Q3_K_M-plus-layers

Use Docker

docker model run hf.co/meshllm/GLM-5.1-Q3_K_M-plus-layers

LM Studio
Jan

vLLM

How to use meshllm/GLM-5.1-Q3_K_M-plus-layers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meshllm/GLM-5.1-Q3_K_M-plus-layers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meshllm/GLM-5.1-Q3_K_M-plus-layers",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/meshllm/GLM-5.1-Q3_K_M-plus-layers

Ollama
How to use meshllm/GLM-5.1-Q3_K_M-plus-layers with Ollama:
```
ollama run hf.co/meshllm/GLM-5.1-Q3_K_M-plus-layers
```

Unsloth Studio

How to use meshllm/GLM-5.1-Q3_K_M-plus-layers 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 meshllm/GLM-5.1-Q3_K_M-plus-layers 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 meshllm/GLM-5.1-Q3_K_M-plus-layers to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for meshllm/GLM-5.1-Q3_K_M-plus-layers to start chatting

Atomic Chat new
Docker Model Runner
How to use meshllm/GLM-5.1-Q3_K_M-plus-layers with Docker Model Runner:
```
docker model run hf.co/meshllm/GLM-5.1-Q3_K_M-plus-layers
```

Lemonade

How to use meshllm/GLM-5.1-Q3_K_M-plus-layers with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meshllm/GLM-5.1-Q3_K_M-plus-layers

Run and chat with the model

lemonade run user.GLM-5.1-Q3_K_M-plus-layers-{{QUANT_TAG}}

List all available models

lemonade list

GLM-5.1-Q3_K_M-plus

Distributed GGUF inference package for Mesh LLM

GGUF layer package for running GLM-5.1-Q3_K_M-plus across a local Mesh LLM cluster.

This package is derived from meshllm/GLM-5.1-Q3_K_M-plus-GGUF and keeps the original GGUF distribution split into per-layer artifacts for distributed inference.

Highlights

Run locally	Pool multiple machines	OpenAI-compatible	Package variant
Private inference on your hardware	Split layers across peers	Serve `/v1/chat/completions` locally	`Q3_K_M` layer package

Model Overview

Property	Value
Source model	meshllm/GLM-5.1-Q3_K_M-plus-GGUF
Model id	`meshllm/GLM-5.1-Q3_K_M-plus-GGUF:Q3_K_M-plus`
Family	GLM
Parameter scale	not recorded
Quantization	`Q3_K_M`
Layer count	79
Activation width	6144
Package size	337.4 GB
Source file	`Q3_K_M-plus/GLM-5.1-Q3_K_M-plus-00001-of-00306.gguf`
Package repo	meshllm/GLM-5.1-Q3_K_M-plus-layers

Recommended Use

Local and private inference with Mesh LLM.
Multi-machine serving when the full GGUF is too large for one host.
OpenAI-compatible chat/completions workflows through Mesh LLM's local API.

For upstream architecture details, chat template guidance, sampling recommendations, license terms, and benchmark notes, see the source model card: meshllm/GLM-5.1-Q3_K_M-plus-GGUF.

Quickstart

# Run this on each machine that should contribute memory/compute.
mesh-llm serve --model "meshllm/GLM-5.1-Q3_K_M-plus-layers" --split

# Check the mesh and discover the OpenAI-compatible model name.
curl -s http://localhost:3131/api/status
curl -s http://localhost:3131/v1/models

# Send an OpenAI-compatible chat request.
curl -s http://localhost:3131/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meshllm/GLM-5.1-Q3_K_M-plus-GGUF:Q3_K_M-plus",
    "messages": [{"role": "user", "content": "Write a tiny hello-world function in Rust."}],
    "max_tokens": 128
  }'

Package Variant

Property	Value
Format	`layer-package`
Canonical source ref	`meshllm/GLM-5.1-Q3_K_M-plus-GGUF@main/Q3_K_M-plus/GLM-5.1-Q3_K_M-plus-00001-of-00306.gguf`
Source revision	`main`
Source SHA-256	`59ce669a82d214395b052ddf1595cd7ecf65784884cdf3a8e70b90954b53ca1e`
Skippy ABI	`0.1.27`
Package manifest SHA-256	`d4b28e3e2c4bb3710dd29b044d9aa9d6658542e9c965ecc2c186e047c8a015e4`

What Is Included

Artifact	Path	Contents	SHA-256
Manifest	`model-package.json`	Package schema, source identity, checksums	`d4b28e3e2c4bb3710dd29b044d9aa9d6658542e9c965ecc2c186e047c8a015e4`
Metadata	`shared/metadata.gguf`	0 tensors, 9.0 MB	`23fb542af787ac627f9a8956e3382fdba84b937f2152310aadc91c043f1e894a`
Embeddings	`shared/embeddings.gguf`	1 tensors, 973.2 MB	`8e311ad51204360c8be08eb06329cd86414ee0c8e38e2b70d7748b134734de68`
Output head	`shared/output.gguf`	2 tensors, 1.8 GB	`91ace2947190cfcbdb3150524983bdc4a0db038cab95ce32416f3ca7b45e7b10`
Transformer layers	`layers/layer-*.gguf`	79 layer artifacts, 1806 tensors, 334.7 GB	`see model-package.json`

Validation

Generated by the Mesh LLM HF Jobs splitter from mesh-llm ref 79f6bc603c74d9335087fa08f06d14d21fa99f33. Each artifact is checksummed as it is written, uploaded to this repository, and removed from the job workspace before the next artifact is produced.

skippy-model-package write-package "/source/Q3_K_M-plus/GLM-5.1-Q3_K_M-plus-00001-of-00306.gguf" --out-dir "/tmp/meshllm-layer-job-meshllm_GLM-5.1-Q3_K_M-plus-layers-199/package"

Model tree for meshllm/GLM-5.1-Q3_K_M-plus-layers

Base model

meshllm/GLM-5.1-Q3_K_M-plus-GGUF

Quantized

(1)

this model

meshllm
/

GLM-5.1-Q3_K_M-plus-layers