Instructions to use Atomic-Germ/Ornith-1.0-35B-Testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Atomic-Germ/Ornith-1.0-35B-Testing 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 Atomic-Germ/Ornith-1.0-35B-Testing:F16
# Run inference directly in the terminal:
llama cli -hf Atomic-Germ/Ornith-1.0-35B-Testing:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Atomic-Germ/Ornith-1.0-35B-Testing:F16
# Run inference directly in the terminal:
llama cli -hf Atomic-Germ/Ornith-1.0-35B-Testing:F16

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 Atomic-Germ/Ornith-1.0-35B-Testing:F16
# Run inference directly in the terminal:
./llama-cli -hf Atomic-Germ/Ornith-1.0-35B-Testing:F16

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 Atomic-Germ/Ornith-1.0-35B-Testing:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Atomic-Germ/Ornith-1.0-35B-Testing:F16

Use Docker

docker model run hf.co/Atomic-Germ/Ornith-1.0-35B-Testing:F16

LM Studio
Jan

vLLM

How to use Atomic-Germ/Ornith-1.0-35B-Testing with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Atomic-Germ/Ornith-1.0-35B-Testing"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atomic-Germ/Ornith-1.0-35B-Testing",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Atomic-Germ/Ornith-1.0-35B-Testing:F16

Ollama
How to use Atomic-Germ/Ornith-1.0-35B-Testing with Ollama:
```
ollama run hf.co/Atomic-Germ/Ornith-1.0-35B-Testing:F16
```

Unsloth Studio

How to use Atomic-Germ/Ornith-1.0-35B-Testing 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 Atomic-Germ/Ornith-1.0-35B-Testing 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 Atomic-Germ/Ornith-1.0-35B-Testing to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Atomic-Germ/Ornith-1.0-35B-Testing to start chatting

How to use Atomic-Germ/Ornith-1.0-35B-Testing with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Atomic-Germ/Ornith-1.0-35B-Testing:F16

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": "Atomic-Germ/Ornith-1.0-35B-Testing:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Atomic-Germ/Ornith-1.0-35B-Testing with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Atomic-Germ/Ornith-1.0-35B-Testing:F16

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 "Atomic-Germ/Ornith-1.0-35B-Testing:F16" \
  --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"

Docker Model Runner
How to use Atomic-Germ/Ornith-1.0-35B-Testing with Docker Model Runner:
```
docker model run hf.co/Atomic-Germ/Ornith-1.0-35B-Testing:F16
```

Lemonade

How to use Atomic-Germ/Ornith-1.0-35B-Testing with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Atomic-Germ/Ornith-1.0-35B-Testing:F16

Run and chat with the model

lemonade run user.Ornith-1.0-35B-Testing-F16

List all available models

lemonade list

Hermes Agent

How to use Atomic-Germ/Ornith-1.0-35B-Testing with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Atomic-Germ/Ornith-1.0-35B-Testing:F16

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 Atomic-Germ/Ornith-1.0-35B-Testing:F16

Run Hermes

hermes

Atomic Chat

WIP not yet working on flm

Ornith-1.0-35B - Q4NX for FastFlowLM (AMD Ryzen AI XDNA2)

Ornith-1.0-35B is converted to Q4NX for hardware-accelerated inference with FastFlowLM on AMD Ryzen AI NPUs.

What is Q4NX?

Q4NX is FastFlowLM's native packed-quantization format - a rearranged Q4_1 layout tuned for the NPU matrix engine's tile sizes and memory access patterns. It is not a GGUF file and it does not run on llama.cpp or Ollama; it is meant exclusively for the FastFlowLM engine on AMD Ryzen AI NPUs.

Requirements

FastFlowLM >= 0.9.46 (flm CLI)
AMD Ryzen AI processor with XDNA2 (NPU2) - Strix Point / Ryzen AI 300 series or later
Linux with the XRT NPU stack installed
~47 GB of unified system memory (Q4NX weights + activations + KV cache)

Files

File	Purpose
model.q4nx	Quantized Q4NX text weights
config.json	FastFlowLM model configuration
tokenizer.json	Tokenizer
tokenizer_config.json	Special tokens and chat template
chat_template.jinja	Chat template (optional)
README.md
flm-add.py	Installer script - registers this model with FastFlowLM

Install and run

This repository ships flm-add.py, a small installer that copies the model into the FastFlowLM user directory and registers the tag qwen3.6-moe:35b-a3b. It never modifies the system FastFlowLM install.

# one-time environment (add these to ~/.bashrc)
export FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json"
export FLM_XCLBIN_PATH="$HOME/.config/flm"
python3 ./flm-add.py Atomic-Germ/Ornith-1.0-35B-NPU2 --family qwen3.6-moe

Kernels

FastFlowLM's NPU kernels (xclbins) are closed source and are not shipped in this repository. This model uses the qwen3.6-moe engine family and is shape-identical to the official qwen3.6-moe:35b-a3b model (Qwen3.6-Moe-35BA3B-NPU2). Point the runtime's xclbin path at the matching xclbins directory (or ship your own) before running.

Serve (OpenAI-compatible)

flm serve ornith:35b --port 8080

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"qwen3.6-moe:35b-a3b","messages":[{"role":"user","content":"Hello!"}],"max_tokens":256}'

Model

Registry tag: ornith:35b
Engine family: qwen3.6-moe
Kernel source: official qwen3.6-moe:35b-a3b (Qwen3.6-Moe-35BA3B-NPU2)
Context length: 262,144 tokens (from config)
Hidden size: 2048
Layers: 40
Intermediate size: 512
Vocabulary: 248320
model.q4nx size: 21.64 GB
Base model: ornith-ai/Ornith-1.0-35B
License: other

Original model card

See the upstream model card for training details, benchmarks, and upstream usage. This repository only contains the Q4NX conversion for FastFlowLM.

Upstream card: ornith-ai/Ornith-1.0-35B

Downloads last month: 27

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

8-bit

16-bit

Model tree for Atomic-Germ/Ornith-1.0-35B-Testing

Base model

ornith-ai/Ornith-1.0-35B

Quantized

(178)

this model