Instructions to use letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound with Transformers:

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

pipe = pipeline("text-generation", model="letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound")
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("letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound")
model = AutoModelForMultimodalLM.from_pretrained("letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound

SGLang

How to use letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound 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 "letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound" \
    --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": "letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound",
		"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 "letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound" \
        --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": "letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound with Docker Model Runner:
```
docker model run hf.co/letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound
```

Ornith-1.5-9B — INT4 AutoRound

INT4/W4A16 AutoRound quantization of ornith-ai/Ornith-1.5-9B.

Quantization

AutoRound: 0.14.2
Weight format: INT4 / W4A16
Group size: 128
Symmetric quantization
Packing format: auto_round:auto_gptq
Calibration dataset: NeelNanda/pile-10k
Calibration samples: 128
Calibration sequence length: 512
Model type: Qwen3.5 multimodal conditional generation

The language-model layers are quantized to INT4. The visual tower and other unsupported non-text layers remain in their original higher-precision format.

Transformers usage

from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)

inputs = processor(
    text="Explain quantization in one sentence.",
    return_tensors="pt",
)
inputs = {k: v.to(model.device) if hasattr(v, "to") else v for k, v in inputs.items()}
output = model.generate(**inputs, max_new_tokens=64)
print(processor.batch_decode(output, skip_special_tokens=True)[0])

Standard vLLM compatibility

The language-model values were not re-quantized. For standard vLLM deployment, use the compatibility-repacked checkpoint layout generated from this artifact. The repackaging normalizes duplicated tensor-name prefixes and tokenizer metadata, keeps the language model INT4/W4A16, and restores the vision tower in its original higher-precision format. Vision input remains supported.

Example deployment arguments:

vllm serve letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound --tensor-parallel-size 2 --trust-remote-code --quantization auto_round --served-model-name Ornith-1.5-9B-INT4-W4A16-AutoRound

Verification

The artifact was verified with Transformers and standard vLLM. Text generation and an image-input request both completed successfully with the compatibility-repacked layout.

Attribution and license

This is a derived quantized artifact of ornith-ai/Ornith-1.5-9B. Please follow the upstream model's MIT license and review the upstream model card for intended use, limitations, and full attribution.

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Safetensors

Model size

4B params

Tensor type

I32

BF16

F16

Model tree for letechlead/Ornith-1.5-9B-INT4-W4A16-AutoRound

Base model

ornith-ai/Ornith-1.5-9B

Quantized

(33)

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