Instructions to use meshllm/DeepSeek-R1-Q4_K_M-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meshllm/DeepSeek-R1-Q4_K_M-layers with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="meshllm/DeepSeek-R1-Q4_K_M-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

llama.cpp

How to use meshllm/DeepSeek-R1-Q4_K_M-layers with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf meshllm/DeepSeek-R1-Q4_K_M-layers
# Run inference directly in the terminal:
llama-cli -hf meshllm/DeepSeek-R1-Q4_K_M-layers

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf meshllm/DeepSeek-R1-Q4_K_M-layers
# Run inference directly in the terminal:
llama-cli -hf meshllm/DeepSeek-R1-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 meshllm/DeepSeek-R1-Q4_K_M-layers
# Run inference directly in the terminal:
./llama-cli -hf meshllm/DeepSeek-R1-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 meshllm/DeepSeek-R1-Q4_K_M-layers
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meshllm/DeepSeek-R1-Q4_K_M-layers

Use Docker

docker model run hf.co/meshllm/DeepSeek-R1-Q4_K_M-layers

LM Studio
Jan

vLLM

How to use meshllm/DeepSeek-R1-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 "meshllm/DeepSeek-R1-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": "meshllm/DeepSeek-R1-Q4_K_M-layers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meshllm/DeepSeek-R1-Q4_K_M-layers

Ollama
How to use meshllm/DeepSeek-R1-Q4_K_M-layers with Ollama:
```
ollama run hf.co/meshllm/DeepSeek-R1-Q4_K_M-layers
```

Unsloth Studio new

How to use meshllm/DeepSeek-R1-Q4_K_M-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/DeepSeek-R1-Q4_K_M-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/DeepSeek-R1-Q4_K_M-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/DeepSeek-R1-Q4_K_M-layers to start chatting

Docker Model Runner
How to use meshllm/DeepSeek-R1-Q4_K_M-layers with Docker Model Runner:
```
docker model run hf.co/meshllm/DeepSeek-R1-Q4_K_M-layers
```

Lemonade

How to use meshllm/DeepSeek-R1-Q4_K_M-layers with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meshllm/DeepSeek-R1-Q4_K_M-layers

Run and chat with the model

lemonade run user.DeepSeek-R1-Q4_K_M-layers-{{QUANT_TAG}}

List all available models

lemonade list

jamesdumay commited on 10 days ago

Commit

61cb4e9

verified ·

1 Parent(s): abacfa1

Add queued Mesh LLM model card

Browse files

Add a Mesh LLM card for the queued layer package and upload the cropped logo asset.

Files changed (2) hide show

README.md +71 -0
mesh-llm-logo.svg +15 -0

README.md ADDED Viewed

	@@ -0,0 +1,71 @@

+---
+library_name: mesh-llm
+base_model:
+- "unsloth/DeepSeek-R1-GGUF"
+pipeline_tag: text-generation
+tags:
+- gguf
+- mesh-llm
+- layer-package
+- skippy
+- distributed-inference
+- local-inference
+- openai-compatible
+- queued
+---
+<div align="center">
+  <a href="https://www.meshllm.cloud">
+    <img src="https://huggingface.co/meshllm/DeepSeek-R1-Q4_K_M-layers/raw/main/mesh-llm-logo.svg" alt="Mesh LLM" width="220">
+  </a>
+  <h1>DeepSeek-R1-Q4_K_M</h1>
+  <p>
+    <strong>Queued GGUF layer 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>
+This repository has been reserved for a Mesh LLM layer package derived from [unsloth/DeepSeek-R1-GGUF](https://huggingface.co/unsloth/DeepSeek-R1-GGUF). The package artifacts are not uploaded yet; once generation completes, this card will be replaced with the full package manifest summary and quickstart.
+## Highlights
+| Run locally | Pool multiple machines | OpenAI-compatible | Package status |
+|---|---|---|---|
+| Private inference on your hardware | Split layers across peers | Serve `/v1/chat/completions` locally | Queued `Q4_K_M` layer package |
+## Model Overview
+| Property | Value |
+|---|---|
+| **Source model** | [unsloth/DeepSeek-R1-GGUF](https://huggingface.co/unsloth/DeepSeek-R1-GGUF) |
+| **Model id** | `unsloth/DeepSeek-R1-GGUF:DeepSeek-R1-Q4_K_M` |
+| **Family** | DeepSeek |
+| **Parameter scale** | not recorded |
+| **Quantization** | `Q4_K_M` |
+| **Status** | Queued for layer-package generation |
+| **Queued at** | `2026-05-08T21:33:13.323775276+00:00` |
+| **Source file** | `DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00001-of-00009.gguf` |
+| **Package repo** | [meshllm/DeepSeek-R1-Q4_K_M-layers](https://huggingface.co/meshllm/DeepSeek-R1-Q4_K_M-layers) |
+| **Queue run** | [https://github.com/Mesh-LLM/mesh-llm/actions/runs/25580536970](https://github.com/Mesh-LLM/mesh-llm/actions/runs/25580536970) |
+## Recommended Use
+- Come back after the layer package job finishes.
+- Use the source model card for architecture details, license terms, and sampling guidance.
+- Use Mesh LLM once this repo contains `model-package.json` and `layers/layer-*.gguf` artifacts.
+## Links
+- Source model: [unsloth/DeepSeek-R1-GGUF](https://huggingface.co/unsloth/DeepSeek-R1-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)

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