Instructions to use Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints

SGLang

How to use Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints 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 "Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints with Docker Model Runner:
```
docker model run hf.co/Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints
```

GI Endoscopy Grounded VLM Checkpoints

Research checkpoints for the GI Endoscopy Grounded VLM project.

Recommended checkpoint

qwen3-vl-8b-lora/sqrt-balanced-seed42/checkpoint-400

Base model: Qwen/Qwen3-VL-8B-Instruct

HyperKvasir validation results:

Accuracy: 0.4620
Macro F1: 0.2441
Balanced accuracy: 0.2684

SO400M bridge ablations

ablations/so400m-direct-bridge/checkpoint-500

Full-validation accuracy: 0.2655
Macro F1: 0.1560
Balanced accuracy: 0.1750

ablations/so400m-fixed64-distillation/final

Validation-100 accuracy: 0.0500
Macro F1: 0.0041
Outcome: single-class collapse
This checkpoint must not be used for deployment.

External checkpoints

SO400M classifier:

Sahibnoor1/gi-siglip2-dino-hyperkvasir-checkpoints

Kvasir-SEG segmentation:

Sahibnoor1/kvasir-siglip2-segmentation-checkpoints

Warning

Research demonstration only. These models are not validated for diagnosis, treatment, or patient management. Do not upload identifiable patient information.

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Model tree for Sahibnoor1/gi-endoscopy-grounded-vlm-checkpoints

Base model

Qwen/Qwen3-VL-8B-Instruct

Finetuned

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