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

DeepSeek-R1-Q4_K_M-layers / README.md

jamesdumay

Upload README.md with huggingface_hub

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metadata

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

DeepSeek-R1-Q4_K_M

Distributed GGUF inference package for Mesh LLM

GGUF layer package for running DeepSeek-R1-Q4_K_M across a local Mesh LLM cluster.

This package is derived from unsloth/DeepSeek-R1-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	`Q4_K_M` layer package

Model Overview

Property	Value
Source model	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`
Layer count	61
Activation width	7168
Package size	377.0 GB
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

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/DeepSeek-R1-GGUF.

Quickstart

# Run this on each machine that should contribute memory/compute.
mesh-llm serve --model "meshllm/DeepSeek-R1-Q4_K_M-layers" --split

# Check the mesh and discover the OpenAI-compatible model name.
curl -s http://localhost:3131/api/status
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": "unsloth/DeepSeek-R1-GGUF:DeepSeek-R1-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/DeepSeek-R1-GGUF@main/DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00001-of-00009.gguf`
Source revision	`main`
Source SHA-256	`d111d9e28b4035e6781906b6451b7866737b4a4ee734baa1575c55d8aa1b4200`
Skippy ABI	`0.1.22`
Package manifest SHA-256	`f5a62c4f2f5427ac6e083d7666313832cb61a2fc5d8dacfc317b540d8ac82e9d`

What Is Included

Artifact	Path	Contents	SHA-256
Manifest	`model-package.json`	Package schema, source identity, checksums	`f5a62c4f2f5427ac6e083d7666313832cb61a2fc5d8dacfc317b540d8ac82e9d`
Metadata	`shared/metadata.gguf`	0 tensors, 5.0 MB	`0e1bf01f20ef69f691126b69c633fd63977cd190bea4182a1c9f41d1537b0ad6`
Embeddings	`shared/embeddings.gguf`	1 tensors, 502.1 MB	`951352774cabdce1e5fe940b30ca85ac8440de68afb6c6eceb451524109991f1`
Output head	`shared/output.gguf`	2 tensors, 730.0 MB	`131fcb580fd1ba667733e39ffc806f819f54928621f40cd47405399a8b9abecb`
Transformer layers	`layers/layer-*.gguf`	61 layer artifacts, 1022 tensors, 375.8 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.

skippy-model-package write-package "/source/DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00001-of-00009.gguf" --out-dir "/tmp/meshllm-layer-job-meshllm_DeepSeek-R1-Q4_K_M-layers-137/package"

meshllm
/

DeepSeek-R1-Q4_K_M-layers