Instructions to use Atomic-Germ/Ornith-1.0-9B-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Atomic-Germ/Ornith-1.0-9B-NPU2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Atomic-Germ/Ornith-1.0-9B-NPU2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Atomic-Germ/Ornith-1.0-9B-NPU2")
model = AutoModelForMultimodalLM.from_pretrained("Atomic-Germ/Ornith-1.0-9B-NPU2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Atomic-Germ/Ornith-1.0-9B-NPU2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Atomic-Germ/Ornith-1.0-9B-NPU2"
# 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-1.0-9B-NPU2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Atomic-Germ/Ornith-1.0-9B-NPU2

SGLang

How to use Atomic-Germ/Ornith-1.0-9B-NPU2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Atomic-Germ/Ornith-1.0-9B-NPU2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atomic-Germ/Ornith-1.0-9B-NPU2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Atomic-Germ/Ornith-1.0-9B-NPU2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atomic-Germ/Ornith-1.0-9B-NPU2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Atomic-Germ/Ornith-1.0-9B-NPU2 with Docker Model Runner:
```
docker model run hf.co/Atomic-Germ/Ornith-1.0-9B-NPU2
```

Ornith-1.0-9B-NPU2 / README.md

Atomic-Germ

Update README.md

449399f verified about 2 months ago

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	---
	license: other
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- fastflowlm
	- q4nx
	- npu
	- qwen3.5
	- 9b
	- minicpm
	base_model:
	- ornith-ai/Ornith-1.0-9B
	base_model_relation: quantized
	quantized_by: Atomic-Germ
	library_name: q4nx
	---

	# Ornith-1.0-9B - Q4NX for FastFlowLM (AMD Ryzen AI XDNA2)

	Ornith-1.0-9B 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](https://fastflowlm.com) 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
	- ~15 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) \|
	\| flm-add.py \| Installer script - registers this model with FastFlowLM \|

	## Install and run

	This repository works with `flm-add`, a small installer that copies the model
	into the FastFlowLM user directory and registers the tag `minicpm4.6:0.8b`. It never
	modifies the system FastFlowLM install.

	`pip install flm-add` or `uv tool install flm-add`

	```bash
	uv tool install flm-add
	flm-add Atomic-Germ/Ornith-1.0-9B-NPU2 --family qwen3.5
	FLM_XCLBIN_PATH="$HOME/.config/flm FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json flm run ornith:9b
	```

	## Kernels

	FastFlowLM's NPU kernels (xclbins) are closed source and are not shipped in this repository. This model uses the `qwen3.5` engine family and is shape-identical to the official `qwen3.5:9b` model (`Qwen3.5-9B-NPU2`). Point the runtime's xclbin path at the matching `xclbins` directory (or ship your own) before running.

	## Serve (OpenAI-compatible)

	```bash
	flm serve ornith:9b --port 8080
	```

	```bash
	curl http://127.0.0.1:8080/v1/chat/completions \
	-H 'Content-Type: application/json' \
	-d '{"model":"ornith:9b","messages":[{"role":"user","content":"Hello!"}],"max_tokens":256}'
	```

	## Model

	- Registry tag: `ornith:9b`
	- Engine family: `qwen3.5`
	- Kernel source: official `qwen3.5:9b` (`Qwen3.5-9B-NPU2`)
	- Context length: 262,144 tokens (from config)
	- Hidden size: 4096
	- Layers: 32
	- Intermediate size: 12288
	- Vocabulary: 248320
	- `model.q4nx` size: 7.11 GB
	- Base model: [ornith-ai/Ornith-1.0-9B](https://huggingface.co/ornith-ai/Ornith-1.0-9B)
	- 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-1.0-9B](https://huggingface.co/ornith-ai/Ornith-1.0-9B)