Instructions to use ornith-ai/Ornith-1.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ornith-ai/Ornith-1.5-9B with Transformers:

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

pipe = pipeline("text-generation", model="ornith-ai/Ornith-1.5-9B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ornith-ai/Ornith-1.5-9B")
model = AutoModelForMultimodalLM.from_pretrained("ornith-ai/Ornith-1.5-9B", 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 = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ornith-ai/Ornith-1.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ornith-ai/Ornith-1.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ornith-ai/Ornith-1.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ornith-ai/Ornith-1.5-9B

SGLang

How to use ornith-ai/Ornith-1.5-9B 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 "ornith-ai/Ornith-1.5-9B" \
    --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": "ornith-ai/Ornith-1.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ornith-ai/Ornith-1.5-9B" \
        --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": "ornith-ai/Ornith-1.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ornith-ai/Ornith-1.5-9B with Docker Model Runner:
```
docker model run hf.co/ornith-ai/Ornith-1.5-9B
```

Can you release 4b version please? )

by naildirect - opened 29 days ago

Discussion

naildirect

29 days ago

Can you release 4b version for low ram devices?

6cf

29 days ago

+1, 4B Please!!!

Pummelchen

28 days ago

•

edited 28 days ago

Low RAM Mac's can stream the 35B model from SSD/NVM with fixed max RAM usage like 1GB/2GB etc.

Check this:
https://github.com/Pummelchen/NVMAI

Ohjaaja

27 days ago

•

edited 27 days ago

There allready was this thread. So bumping.

Thank you guys for these models. I am having great time with them and i am sure many others do also. Model request for small ornith based on qwen3.5 4b is in my mind to be used on laptop on the road for small editing etc. That would complement your portfolio of models quite nice.

Pummelchen: Llama.cpp can too stream from nvme but those are slow technques and wear nvme but thanks.

Pummelchen

26 days ago

Llama.cpp has no max ram limiter when you stream. Plus NVMAI is much faster. And NVMs are designed to run for years under max load, no problem at all :)

ai4th3w1n

14 days ago

I agree, a 4B would make agentic spawns much more doable, or even allow full kv cache offload. The 9B is already so capable, the same training process to produce a 4B might yield even more impressive results (for 4B class).

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