Instructions to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-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/MiniMax-M2.7-UD-Q4_K_XL-layers
# Run inference directly in the terminal:
llama cli -hf meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers
# Run inference directly in the terminal:
llama cli -hf meshllm/MiniMax-M2.7-UD-Q4_K_XL-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/MiniMax-M2.7-UD-Q4_K_XL-layers
# Run inference directly in the terminal:
./llama-cli -hf meshllm/MiniMax-M2.7-UD-Q4_K_XL-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/MiniMax-M2.7-UD-Q4_K_XL-layers
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

Use Docker

docker model run hf.co/meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

LM Studio
Jan

vLLM

How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

Ollama
How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers with Ollama:
```
ollama run hf.co/meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers
```

Unsloth Studio

How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-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/MiniMax-M2.7-UD-Q4_K_XL-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/MiniMax-M2.7-UD-Q4_K_XL-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/MiniMax-M2.7-UD-Q4_K_XL-layers to start chatting

How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

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": "meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers with Docker Model Runner:
```
docker model run hf.co/meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers
```

Lemonade

How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

Run and chat with the model

lemonade run user.MiniMax-M2.7-UD-Q4_K_XL-layers-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

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 meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers

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 "meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers" \
  --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"

jamesdumay commited on May 25

Commit

fe69877

verified ·

1 Parent(s): b50f1c6

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +128 -0

README.md ADDED Viewed

	@@ -0,0 +1,128 @@

+---
+library_name: mesh-llm
+base_model:
+- "unsloth/MiniMax-M2.7-GGUF"
+pipeline_tag: text-generation
+tags:
+- gguf
+- mesh-llm
+- layer-package
+- skippy
+- distributed-inference
+- local-inference
+- openai-compatible
+---
+<div align="center">
+  <a href="https://www.meshllm.cloud">
+    <img src="https://github.com/Mesh-LLM/mesh-llm/raw/main/docs/mesh-llm-logo.svg" alt="Mesh LLM" width="220">
+  </a>
+  <h1>MiniMax-M2.7-UD-Q4_K_XL</h1>
+  <p>
+    <strong>Distributed GGUF inference package for Mesh LLM</strong>
+  </p>
+  <p>
+    <a href="https://www.meshllm.cloud"><img alt="Website" src="https://img.shields.io/badge/Website-meshllm.cloud-111111?style=for-the-badge"></a>
+    <a href="https://github.com/Mesh-LLM/mesh-llm"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-Mesh--LLM-24292f?style=for-the-badge&logo=github"></a>
+    <a href="https://discord.gg/rs6fmc63eN"><img alt="Discord" src="https://img.shields.io/badge/Discord-Join-5865F2?style=for-the-badge&logo=discord&logoColor=white"></a>
+  </p>
+</div>
+GGUF layer package for running **MiniMax-M2.7-UD-Q4_K_XL** across a local Mesh LLM cluster.
+This package is derived from [unsloth/MiniMax-M2.7-GGUF](https://huggingface.co/unsloth/MiniMax-M2.7-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 | `UD-Q4_K_XL` layer package |
+## Model Overview
+| Property | Value |
+|---|---|
+| **Source model** | [unsloth/MiniMax-M2.7-GGUF](https://huggingface.co/unsloth/MiniMax-M2.7-GGUF) |
+| **Model id** | `unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_XL` |
+| **Family** | MiniMax |
+| **Parameter scale** | not recorded |
+| **Quantization** | `UD-Q4_K_XL` |
+| **Layer count** | 62 |
+| **Activation width** | 3072 |
+| **Package size** | 131.6 GB |
+| **Source file** | `UD-Q4_K_XL/MiniMax-M2.7-UD-Q4_K_XL-00001-of-00004.gguf` |
+| **Package repo** | [meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers](https://huggingface.co/meshllm/MiniMax-M2.7-UD-Q4_K_XL-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: [unsloth/MiniMax-M2.7-GGUF](https://huggingface.co/unsloth/MiniMax-M2.7-GGUF).
+## Quickstart
+```bash
+# Run this on each machine that should contribute memory/compute.
+mesh-llm serve --model "meshllm/MiniMax-M2.7-UD-Q4_K_XL-layers" --split
+```
+```bash
+# Check the mesh and discover the OpenAI-compatible model name.
+curl -s http://localhost:3131/api/status
+curl -s http://localhost:3131/v1/models
+```
+```bash
+# Send an OpenAI-compatible chat request.
+curl -s http://localhost:3131/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_XL",
+    "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** | `unsloth/MiniMax-M2.7-GGUF@main/UD-Q4_K_XL/MiniMax-M2.7-UD-Q4_K_XL-00001-of-00004.gguf` |
+| **Source revision** | `main` |
+| **Source SHA-256** | `92986e39a0c0b5f12c2c9b6a811dad59e3317caaf1b7ad5c7f0d7d12abc4a6e8` |
+| **Skippy ABI** | `0.1.24` |
+| **Package manifest SHA-256** | `79cf4a0615e5dbdafe779d16b35f711580382b5e9fb213366e44cd12c62cca36` |
+## What Is Included
+| Artifact | Path | Contents | SHA-256 |
+|---|---|---|---|
+| Manifest | `model-package.json` | Package schema, source identity, checksums | `79cf4a0615e5dbdafe779d16b35f711580382b5e9fb213366e44cd12c62cca36` |
+| Metadata | `shared/metadata.gguf` | 0 tensors, 7.9 MB | `48b08e5c2318a0216ff171917974ad2198a440c15596717659a8f4fd61959fc0` |
+| Embeddings | `shared/embeddings.gguf` | 1 tensors, 630.6 MB | `248cc26559607a1c48bb261dc5c040091414c1b6a6d3e6f7a84cb138f3f1f028` |
+| Output head | `shared/output.gguf` | 2 tensors, 630.6 MB | `0b7ff0224405f71f57c6c968af856909cfdb66f7870253de276832fc2531c203` |
+| Transformer layers | `layers/layer-*.gguf` | 62 layer artifacts, 806 tensors, 130.4 GB | `see model-package.json` |
+## Validation
+Generated by the Mesh LLM HF Jobs splitter from `mesh-llm` ref `main`.
+Each artifact is checksummed as it is written, uploaded to this repository, and removed from the job workspace before the next artifact is produced.
+```bash
+skippy-model-package write-package "/source/UD-Q4_K_XL/MiniMax-M2.7-UD-Q4_K_XL-00001-of-00004.gguf" --out-dir "/tmp/meshllm-layer-job-meshllm_MiniMax-M2.7-UD-Q4_K_XL-layers-194/package"
+```
+## Links
+- Source model: [unsloth/MiniMax-M2.7-GGUF](https://huggingface.co/unsloth/MiniMax-M2.7-GGUF)
+- Mesh LLM website: [meshllm.cloud](https://www.meshllm.cloud)
+- Mesh LLM: [github.com/Mesh-LLM/mesh-llm](https://github.com/Mesh-LLM/mesh-llm)
+- Discord: [discord.gg/rs6fmc63eN](https://discord.gg/rs6fmc63eN)
+- Package catalog: [meshllm/catalog](https://huggingface.co/datasets/meshllm/catalog)
+- Package format: [layer-package-repos.md](https://github.com/Mesh-LLM/mesh-llm/blob/main/docs/specs/layer-package-repos.md)