Instructions to use meshllm/Ornith-1.5-35B-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

How to use meshllm/Ornith-1.5-35B-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 meshllm/Ornith-1.5-35B-Q4_K_M-layers
# Run inference directly in the terminal:
llama cli -hf meshllm/Ornith-1.5-35B-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 meshllm/Ornith-1.5-35B-Q4_K_M-layers
# Run inference directly in the terminal:
llama cli -hf meshllm/Ornith-1.5-35B-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/Ornith-1.5-35B-Q4_K_M-layers
# Run inference directly in the terminal:
./llama-cli -hf meshllm/Ornith-1.5-35B-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/Ornith-1.5-35B-Q4_K_M-layers
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meshllm/Ornith-1.5-35B-Q4_K_M-layers

Use Docker

docker model run hf.co/meshllm/Ornith-1.5-35B-Q4_K_M-layers

LM Studio
Jan

vLLM

How to use meshllm/Ornith-1.5-35B-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/Ornith-1.5-35B-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/Ornith-1.5-35B-Q4_K_M-layers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meshllm/Ornith-1.5-35B-Q4_K_M-layers

Ollama
How to use meshllm/Ornith-1.5-35B-Q4_K_M-layers with Ollama:
```
ollama run hf.co/meshllm/Ornith-1.5-35B-Q4_K_M-layers
```
Unsloth Desktop

How to use meshllm/Ornith-1.5-35B-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 meshllm/Ornith-1.5-35B-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": "meshllm/Ornith-1.5-35B-Q4_K_M-layers"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use meshllm/Ornith-1.5-35B-Q4_K_M-layers with Docker Model Runner:
```
docker model run hf.co/meshllm/Ornith-1.5-35B-Q4_K_M-layers
```

Lemonade

How to use meshllm/Ornith-1.5-35B-Q4_K_M-layers with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meshllm/Ornith-1.5-35B-Q4_K_M-layers

Run and chat with the model

lemonade run user.Ornith-1.5-35B-Q4_K_M-layers-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use meshllm/Ornith-1.5-35B-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 meshllm/Ornith-1.5-35B-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 meshllm/Ornith-1.5-35B-Q4_K_M-layers

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use meshllm/Ornith-1.5-35B-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 meshllm/Ornith-1.5-35B-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 "meshllm/Ornith-1.5-35B-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"

Ornith-1.5-35B-Q4_K_M

Distributed GGUF inference package for Mesh LLM

GGUF layer package for running Ornith-1.5-35B-Q4_K_M across a local Mesh LLM cluster.

This package is derived from ornith-ai/Ornith-1.5-35B-A3B-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	ornith-ai/Ornith-1.5-35B-A3B-GGUF
Model id	`ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M`
Family	Ornith
Parameter scale	35B
Quantization	`Q4_K_M`
Layer count	41
Activation width	not recorded
Package size	0 B
Source file	`Ornith-1.5-35B-Q4_K_M.gguf`
Package repo	meshllm/Ornith-1.5-35B-Q4_K_M-layers
License	`mit` from ornith-ai/Ornith-1.5-35B-A3B-GGUF

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: ornith-ai/Ornith-1.5-35B-A3B-GGUF.

Quickstart

# Run this on each machine that should contribute memory/compute.
mesh-llm serve --model "meshllm/Ornith-1.5-35B-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": "ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M",
    "messages": [{"role": "user", "content": "Write a tiny hello-world function in Rust."}],
    "max_tokens": 128
  }'

Package Variant

Property	Value
Format	`gguf`
Canonical source ref	`ornith-ai/Ornith-1.5-35B-A3B-GGUF@12393612fd4f730ff5aadc23e9b8f9648aa49ceb/Ornith-1.5-35B-Q4_K_M.gguf`
Source revision	`12393612fd4f730ff5aadc23e9b8f9648aa49ceb`
Source SHA-256	`42739874cc2ccfdb8523b23fbe52e29b2a7555c8176737ca9ca0b5d59859d41f`
Skippy ABI	`not recorded`
Package manifest SHA-256	`397df60b3e66c79633ca741e081e5c53407762df28a3fac4ee3714ddfccd12a9`

What Is Included

Artifact	Path	Contents	SHA-256
Manifest	`model-package.json`	Package schema, source identity, checksums	`397df60b3e66c79633ca741e081e5c53407762df28a3fac4ee3714ddfccd12a9`

Validation

Generated by the Mesh LLM HF Jobs splitter from mesh-llm ref 29bdf713d769a45cd299f4a4f7cea5333d700efd. 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 "/hf-cache/Ornith-1.5-35B-Q4_K_M.gguf" --out-dir "/tmp/meshllm-layer-job-meshllm_Ornith-1.5-35B-Q4_K_M-layers-1/package"

Model tree for meshllm/Ornith-1.5-35B-Q4_K_M-layers

Base model

ornith-ai/Ornith-1.5-35B-A3B-GGUF

Quantized

(2)

this model

meshllm
/

Ornith-1.5-35B-Q4_K_M-layers

Ornith-1.5-35B-Q4_K_M

Highlights

Model Overview

Recommended Use

Quickstart

Package Variant

What Is Included

Validation

Links

Model tree for meshllm/Ornith-1.5-35B-Q4_K_M-layers