Instructions to use meshllm/GLM-4.7-Flash-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meshllm/GLM-4.7-Flash-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="meshllm/GLM-4.7-Flash-MTP-GGUF",
	filename="GLM-4.7-Flash-MTP-Q4_K_M.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-4.7-Flash-MTP-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 meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M

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-4.7-Flash-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M

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-4.7-Flash-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use meshllm/GLM-4.7-Flash-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meshllm/GLM-4.7-Flash-MTP-GGUF"
# 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-4.7-Flash-MTP-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M

Ollama
How to use meshllm/GLM-4.7-Flash-MTP-GGUF with Ollama:
```
ollama run hf.co/meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M
```

Unsloth Studio

How to use meshllm/GLM-4.7-Flash-MTP-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 meshllm/GLM-4.7-Flash-MTP-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 meshllm/GLM-4.7-Flash-MTP-GGUF 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-4.7-Flash-MTP-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use meshllm/GLM-4.7-Flash-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M
```

Lemonade

How to use meshllm/GLM-4.7-Flash-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meshllm/GLM-4.7-Flash-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-4.7-Flash-MTP-GGUF-Q4_K_M

List all available models

lemonade list

GLM-4.7-Flash-MTP-GGUF

MTP GGUF artifact for Mesh LLM

GGUF artifact for running GLM-4.7 Flash native multi-token prediction (MTP) with Mesh LLM.

This repository contains the Q4_K_M GGUF artifact for GLM-4.7 Flash with native MTP metadata and tensors.

Highlights

Run locally	Native MTP	OpenAI-compatible	Artifact variant
Private inference on your hardware	GLM-4.7 Flash `N+1` MTP support	Serve `/v1/chat/completions` locally	`Q4_K_M` GGUF

Model Overview

Property	Value
Source model	zai-org/GLM-4.7-Flash
Model id	`meshllm/GLM-4.7-Flash-MTP-GGUF`
Family	GLM
Parameter scale	31.2B reported by Hub GGUF metadata
Quantization	`Q4_K_M`
GGUF architecture	`deepseek2`
Context length	202,752
Artifact size	18.9 GB
Source file	`GLM-4.7-Flash-MTP-Q4_K_M.gguf`
Artifact repo	meshllm/GLM-4.7-Flash-MTP-GGUF

Recommended Use

Local and private inference with Mesh LLM.
Native GLM-4.7 Flash MTP serving.
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: zai-org/GLM-4.7-Flash.

Quickstart

# Download the Q4_K_M MTP GGUF artifact.
hf download meshllm/GLM-4.7-Flash-MTP-GGUF \
  --include 'GLM-4.7-Flash-MTP-Q4_K_M.gguf'

# Run locally with Mesh LLM.
mesh-llm serve --model "meshllm/GLM-4.7-Flash-MTP-GGUF"

# Check the local OpenAI-compatible model list.
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-4.7-Flash-MTP-GGUF",
    "messages": [{"role": "user", "content": "Write a tiny hello-world function in Rust."}],
    "max_tokens": 128
  }'

Artifact Variant

Property	Value
Format	`gguf`
Canonical source ref	`meshllm/GLM-4.7-Flash-MTP-GGUF@main/GLM-4.7-Flash-MTP-Q4_K_M.gguf`
Quantization	`Q4_K_M`
BOS token	`[gMASK]`
EOS token	`<\|endoftext\|>`
Conversion revision note	`60a9f590d335a032fb79e4a9fc07bce8212a33ba`

What Is Included

Artifact	Path	Contents
GGUF model	`GLM-4.7-Flash-MTP-Q4_K_M.gguf`	Q4_K_M GLM-4.7 Flash GGUF with native MTP tensors and metadata
Original note	`README.txt`	Proof-artifact note
Q4 inspection	`glm47-mtp-q4-gguf.txt`	GGUF inspection output for the Q4_K_M artifact
F16 inspection	`glm47-mtp-f16-gguf.txt`	GGUF inspection output for the F16 conversion artifact
Conversion revision	`llama-cpp-revision.txt`	Revision recorded for the MTP GGUF conversion path

Why MTP Matters

MTP, or multi-token prediction, lets a model predict the next token and an extra future token in the same decode step. That gives the runtime a candidate sequence to verify instead of waiting for one full model pass per token.

For GLM-4.7 Flash, native MTP proposes N+1: one ordinary next token plus one additional predicted token. Mesh LLM can use that extra token as a speculative candidate during local inference.

Validation

The repository includes GGUF inspection notes for the published artifact:

glm47-mtp-q4-gguf.txt
glm47-mtp-f16-gguf.txt
llama-cpp-revision.txt

Model tree for meshllm/GLM-4.7-Flash-MTP-GGUF

Base model

zai-org/GLM-4.7-Flash

Quantized

(88)

this model

meshllm
/

GLM-4.7-Flash-MTP-GGUF