Text Generation
GGUF
mesh-llm
layer-package
skippy
distributed-inference
local-inference
openai-compatible
imatrix
conversational
Instructions to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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 ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers # Run inference directly in the terminal: llama cli -hf ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers # Run inference directly in the terminal: llama cli -hf ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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 ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers # Run inference directly in the terminal: ./llama-cli -hf ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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 ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers # Run inference directly in the terminal: ./build/bin/llama-cli -hf ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers
Use Docker
docker model run hf.co/ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers
- LM Studio
- Jan
- vLLM
How to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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": "ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers
- Ollama
How to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers with Ollama:
ollama run hf.co/ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers
- Unsloth Desktop
- Pi
How to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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": "ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers with Docker Model Runner:
docker model run hf.co/ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers
- Lemonade
How to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers
Run and chat with the model
lemonade run user.gemma-4-26B-A4B-it-UD-Q4_K_M-layers-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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 ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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 ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-layers with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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 "ashton25549/gemma-4-26B-A4B-it-UD-Q4_K_M-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"
File size: 5,283 Bytes
5b82874 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | ---
library_name: mesh-llm
base_model:
- "unsloth/gemma-4-26B-A4B-it-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://huggingface.co/meshllm/gemma-4-26B-A4B-it-UD-Q4_K_M-layers/raw/main/mesh-llm-logo.svg" alt="Mesh LLM" width="220">
</a>
<h1>gemma-4-26B-A4B-it-UD-Q4_K_M</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 **gemma-4-26B-A4B-it-UD-Q4_K_M** across a local Mesh LLM cluster.
This package is derived from [unsloth/gemma-4-26B-A4B-it-GGUF](https://huggingface.co/unsloth/gemma-4-26B-A4B-it-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_M` layer package |
## Model Overview
| Property | Value |
|---|---|
| **Source model** | [unsloth/gemma-4-26B-A4B-it-GGUF](https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF) |
| **Model id** | `unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M` |
| **Family** | Gemma |
| **Parameter scale** | 26B-A4B |
| **Quantization** | `UD-Q4_K_M` |
| **Layer count** | 30 |
| **Activation width** | 2816 |
| **Package size** | 16.3 GB |
| **Source file** | `gemma-4-26B-A4B-it-UD-Q4_K_M.gguf` |
| **Package repo** | [meshllm/gemma-4-26B-A4B-it-UD-Q4_K_M-layers](https://huggingface.co/meshllm/gemma-4-26B-A4B-it-UD-Q4_K_M-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/gemma-4-26B-A4B-it-GGUF](https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF).
## Quickstart
```bash
# Run this on each machine that should contribute memory/compute.
mesh-llm serve --model "meshllm/gemma-4-26B-A4B-it-UD-Q4_K_M-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/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M",
"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/gemma-4-26B-A4B-it-GGUF@main/gemma-4-26B-A4B-it-UD-Q4_K_M.gguf` |
| **Source revision** | `main` |
| **Source SHA-256** | `34c746b1d50ab813e29cd46c4796e3f43c741901a582f93a67b55b9fc9687b35` |
| **Skippy ABI** | `0.1.22` |
| **Package manifest SHA-256** | `23c1e32d371c3f50aa393e046894d1f893f22d99f0ccee91552be17cf9c88b65` |
## What Is Included
| Artifact | Path | Contents | SHA-256 |
|---|---|---|---|
| Manifest | `model-package.json` | Package schema, source identity, checksums | `23c1e32d371c3f50aa393e046894d1f893f22d99f0ccee91552be17cf9c88b65` |
| Metadata | `shared/metadata.gguf` | 1 tensors, 15.1 MB | `4ea990cfa36597971790aa1dcc4658cd8c946685b3d16dbaac4f685efb382002` |
| Embeddings | `shared/embeddings.gguf` | 2 tensors, 763.1 MB | `6eb39617e6999290dd4c338cdcadf98ea42f948812fb4aeb0eab50735c7e0dd3` |
| Output head | `shared/output.gguf` | 2 tensors, 15.1 MB | `88f7262cffae57f67ffb7b8db8fa9c04439d43a3af7d53fa3df81f0c6511e522` |
| Transformer layers | `layers/layer-*.gguf` | 30 layer artifacts, 685 tensors, 15.5 GB | `see model-package.json` |
## Validation
Generated by the Mesh LLM HF Jobs splitter from `mesh-llm` ref `main` and validated before upload:
```bash
skippy-model-package validate-package "/source/gemma-4-26B-A4B-it-UD-Q4_K_M.gguf" "$PACKAGE_DIR"
```
## Links
- Source model: [unsloth/gemma-4-26B-A4B-it-GGUF](https://huggingface.co/unsloth/gemma-4-26B-A4B-it-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)
|