Instructions to use Div97/orena-focus-segment-fullvis-w64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Div97/orena-focus-segment-fullvis-w64 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Div97/orena-focus-segment-fullvis-w64")
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("Div97/orena-focus-segment-fullvis-w64")
model = AutoModelForMultimodalLM.from_pretrained("Div97/orena-focus-segment-fullvis-w64", 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 Div97/orena-focus-segment-fullvis-w64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Div97/orena-focus-segment-fullvis-w64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Div97/orena-focus-segment-fullvis-w64",
		"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/Div97/orena-focus-segment-fullvis-w64

SGLang

How to use Div97/orena-focus-segment-fullvis-w64 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 "Div97/orena-focus-segment-fullvis-w64" \
    --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": "Div97/orena-focus-segment-fullvis-w64",
		"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 "Div97/orena-focus-segment-fullvis-w64" \
        --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": "Div97/orena-focus-segment-fullvis-w64",
		"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 Div97/orena-focus-segment-fullvis-w64 with Docker Model Runner:
```
docker model run hf.co/Div97/orena-focus-segment-fullvis-w64
```

DISCOVR-SEGMENT FullVis-W64

Merged bfloat16 checkpoint used by DISCOVR-SEGMENT, submitted to the ORena SAVE FOCUS 2026 SEGMENT track by team Incision Impossible.

Base: Qwen/Qwen3-VL-4B-Instruct
Adaptation: language, vision-encoder, and merger LoRA
Merge: PEFT merge_and_unload
Serving budget: 64 frames
Model SHA-256: 622fd66547b2ad88f9fcf9c74a22450f44b4c88cef8fcf1a9b464de2a51dcff3
Selected challenge image: b74f595d-06d4-488a-b76b-217544cf8e55

Source and documentation:

Training summary

FullVis-W64 was trained in two stages:

one-epoch surgical scene-literacy warm-up on a 238,925-row SSG-VQA / CholecT45 manifest, with language rank 16, vision rank 16, and merger rank 64; and
three scheduled epochs of continued all-track FOCUS fine-tuning on 34,290 HeiCo and LapChole training rows with a maximum of 64 frames.

The detailed document records exact data counts and hashes, frame generation, optimizer settings, prompt construction, selected artifact hashes, known historical dataset-revision differences, and official pre-evaluation metrics.

Inference method

DISCOVR-SEGMENT uses question-dependent temporal routing:

ordinary questions sample 64 frames across the complete segment;
questions containing timestamps use up to two ±30-second evidence windows;
single-timestamp questions receive a second 64-frame pass in a 30-second window around the first prediction;
temporal frames receive an absolute-procedure-time overlay; and
output is normalized to the inferred answer format.

Intended use and limitations

The checkpoint is intended for non-commercial research and challenge reproduction. It is not a medical device and must not be used for clinical decision making. No patient videos or raw challenge annotations are distributed here.

License and data terms

The Qwen3-VL base model and released source code use Apache-2.0. Training also used SSG-VQA, whose repository specifies CC BY-NC-SA 4.0 for non-commercial scientific research, plus challenge datasets governed by their respective owners. The license: other metadata reflects these mixed terms; it does not replace any source-dataset license or access agreement.

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Safetensors

Model size

4B params

Tensor type

BF16

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