Instructions to use meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers", filename="layers/layer-000.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers # Run inference directly in the terminal: llama cli -hf meshllm/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers # Run inference directly in the terminal: llama cli -hf meshllm/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers # Run inference directly in the terminal: ./llama-cli -hf meshllm/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers # Run inference directly in the terminal: ./build/bin/llama-cli -hf meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers
Use Docker
docker model run hf.co/meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers
- LM Studio
- Jan
- vLLM
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers
- Ollama
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers with Ollama:
ollama run hf.co/meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers
- Unsloth Studio
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers to start chatting
- Pi
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers with Docker Model Runner:
docker model run hf.co/meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers
- Lemonade
How to use meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers
Run and chat with the model
lemonade run user.Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers-{{QUANT_TAG}}List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files|
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---
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library_name: mesh-llm
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base_model:
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- "unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF"
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pipeline_tag: text-generation
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tags:
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- gguf
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- mesh-llm
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- layer-package
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- skippy
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- distributed-inference
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- local-inference
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- openai-compatible
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---
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<div align="center">
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<a href="https://www.meshllm.cloud">
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<img src="https://github.com/Mesh-LLM/mesh-llm/raw/main/docs/mesh-llm-logo.svg" alt="Mesh LLM" width="220">
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</a>
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<h1>Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL</h1>
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<p>
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<strong>Distributed GGUF inference package for Mesh LLM</strong>
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</p>
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<p>
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<a href="https://www.meshllm.cloud"><img alt="Website" src="https://img.shields.io/badge/Website-meshllm.cloud-111111?style=for-the-badge"></a>
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<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>
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<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>
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</p>
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</div>
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GGUF layer package for running **Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL** across a local Mesh LLM cluster.
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This package is derived from [unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF](https://huggingface.co/unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF) and keeps the original GGUF distribution split into per-layer artifacts for distributed inference.
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## Highlights
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| Run locally | Pool multiple machines | OpenAI-compatible | Package variant |
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|---|---|---|---|
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| Private inference on your hardware | Split layers across peers | Serve `/v1/chat/completions` locally | `UD-Q4_K_XL` layer package |
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## Model Overview
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| Property | Value |
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|---|---|
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| **Source model** | [unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF](https://huggingface.co/unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF) |
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| **Model id** | `unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL` |
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| **Family** | Devstral |
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| **Parameter scale** | 24B |
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| **Quantization** | `UD-Q4_K_XL` |
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| **Layer count** | 40 |
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| **Activation width** | 5120 |
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| **Package size** | 13.8 GB |
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| **Source file** | `Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL.gguf` |
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| **Package repo** | [meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers](https://huggingface.co/meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers) |
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## Recommended Use
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- Local and private inference with Mesh LLM.
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- Multi-machine serving when the full GGUF is too large for one host.
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- OpenAI-compatible chat/completions workflows through Mesh LLM's local API.
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For upstream architecture details, chat template guidance, sampling recommendations, license terms, and benchmark notes, see the source model card: [unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF](https://huggingface.co/unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF).
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## Quickstart
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```bash
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# Run this on each machine that should contribute memory/compute.
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mesh-llm serve --model "meshllm/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers" --split
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```
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```bash
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# Check the mesh and discover the OpenAI-compatible model name.
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curl -s http://localhost:3131/api/status
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curl -s http://localhost:3131/v1/models
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```
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```bash
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# Send an OpenAI-compatible chat request.
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curl -s http://localhost:3131/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL",
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"messages": [{"role": "user", "content": "Write a tiny hello-world function in Rust."}],
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"max_tokens": 128
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}'
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```
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## Package Variant
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| Property | Value |
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|---|---|
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| **Format** | `layer-package` |
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| **Canonical source ref** | `unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF@main/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL.gguf` |
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| **Source revision** | `main` |
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| **Source SHA-256** | `b44e34b78180fc3ab1abbe1edad9f1f3926fdca10eed3bfae168b065e683f6cd` |
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| **Skippy ABI** | `0.1.25` |
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| **Package manifest SHA-256** | `1527887657160605200ea0d37353c695f1fe7d74b8d310b1de2a6e371102b67f` |
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## What Is Included
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| Artifact | Path | Contents | SHA-256 |
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|---|---|---|---|
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| Manifest | `model-package.json` | Package schema, source identity, checksums | `1527887657160605200ea0d37353c695f1fe7d74b8d310b1de2a6e371102b67f` |
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| Metadata | `shared/metadata.gguf` | 0 tensors, 8.0 MB | `86fec00c5793bcf438dd2d1c5f75f782c79ea84cf6aaa4450bcc2bd146341e68` |
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| Embeddings | `shared/embeddings.gguf` | 1 tensors, 368.0 MB | `5cdea2e2b47b8d751d31151a3f63196b8723fe8588621228b94c126cc9977ea7` |
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| Output head | `shared/output.gguf` | 2 tensors, 533.0 MB | `3c7be3f6156a76320542e283b933580a1988415c2696a3b8b37ad58f5a68be0b` |
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| Transformer layers | `layers/layer-*.gguf` | 40 layer artifacts, 360 tensors, 13.0 GB | `see model-package.json` |
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## Validation
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Generated by the Mesh LLM HF Jobs splitter from `mesh-llm` ref `main`.
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Each artifact is checksummed as it is written, uploaded to this repository, and removed from the job workspace before the next artifact is produced.
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```bash
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skippy-model-package write-package "/source/Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL.gguf" --out-dir "/tmp/meshllm-layer-job-meshllm_Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL-layers-199/package"
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```
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## Links
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- Source model: [unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF](https://huggingface.co/unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF)
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- Mesh LLM website: [meshllm.cloud](https://www.meshllm.cloud)
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- Mesh LLM: [github.com/Mesh-LLM/mesh-llm](https://github.com/Mesh-LLM/mesh-llm)
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- Discord: [discord.gg/rs6fmc63eN](https://discord.gg/rs6fmc63eN)
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- Package catalog: [meshllm/catalog](https://huggingface.co/datasets/meshllm/catalog)
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- Package format: [layer-package-repos.md](https://github.com/Mesh-LLM/mesh-llm/blob/main/docs/specs/layer-package-repos.md)
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