Instructions to use dipankar500/VaidenceVLM-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dipankar500/VaidenceVLM-1.0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="dipankar500/VaidenceVLM-1.0")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dipankar500/VaidenceVLM-1.0", device_map="auto")

PEFT
How to use dipankar500/VaidenceVLM-1.0 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dipankar500/VaidenceVLM-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dipankar500/VaidenceVLM-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dipankar500/VaidenceVLM-1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dipankar500/VaidenceVLM-1.0

SGLang

How to use dipankar500/VaidenceVLM-1.0 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 "dipankar500/VaidenceVLM-1.0" \
    --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": "dipankar500/VaidenceVLM-1.0",
		"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 "dipankar500/VaidenceVLM-1.0" \
        --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": "dipankar500/VaidenceVLM-1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dipankar500/VaidenceVLM-1.0 with Docker Model Runner:
```
docker model run hf.co/dipankar500/VaidenceVLM-1.0
```

VaidenceVLM-1.0 — Research Preview

VaidenceVLM-1.0 is a research-only PEFT/QLoRA adapter for OpenGVLab/InternVL3_5-4B. This repository contains adapter weights, not a standalone 4B base model. It requires the named base model, a CUDA GPU, and trust_remote_code=True.

It is not a medical device and must not be used for diagnosis, triage, treatment, or patient care.

Quick start

git clone https://huggingface.co/dipankar500/VaidenceVLM-1.0
cd VaidenceVLM-1.0
python -m pip install -r requirements.txt
python inference.py --adapter . --image /path/to/image.jpg --question "Describe visible findings and state uncertainty."

SYSTEM_PROMPT.txt is a safety-oriented interactive prompt. It was not used for the reported benchmark scores, so do not prepend it when reproducing those results.

Reproducible generative evaluation — not official leaderboard metrics

Benchmark / split	Questions	Normalized exact match	Token F1	Yes/No exact match	Errors
SLAKE English test	1061	57.49%	62.33%	80.85%	0
VQA-RAD test	32	53.12%	60.64%	77.78%	0

Metrics use our released greedy generative evaluator and custom normalized answer matching. They are not official benchmark leaderboard scores, clinical accuracy, evidence of state of the art, or a comparison with Gemini or another proprietary model. See EVALUATION.md.

Repository contents

adapter_model.safetensors, adapter_config.json: PEFT adapter weights/configuration.
tokenizer/: recovered tokenizer files.
inference.py: verified local inference entry point.
SYSTEM_PROMPT.txt: default interactive research-safety prompt.
EVALUATION.md, TRAINING_SUMMARY.md, LIMITATIONS.md: transparent release documentation.

Citation

See CITATION.cff. Cite InternVL 3.5 and the original datasets in research use.

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for dipankar500/VaidenceVLM-1.0

Base model

OpenGVLab/InternVL3_5-4B-Pretrained

Finetuned

OpenGVLab/InternVL3_5-4B-Instruct

Finetuned

OpenGVLab/InternVL3_5-4B-MPO

Finetuned

OpenGVLab/InternVL3_5-4B

Adapter

(1)

this model