Instructions to use doctoria/doctoria-rally-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use doctoria/doctoria-rally-ai with Transformers:

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

pipe = pipeline("image-text-to-text", model="doctoria/doctoria-rally-ai")
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("doctoria/doctoria-rally-ai")
model = AutoModelForMultimodalLM.from_pretrained("doctoria/doctoria-rally-ai", 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 doctoria/doctoria-rally-ai with vLLM:

Install from pip and serve model

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

SGLang

How to use doctoria/doctoria-rally-ai 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 "doctoria/doctoria-rally-ai" \
    --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": "doctoria/doctoria-rally-ai",
		"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 "doctoria/doctoria-rally-ai" \
        --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": "doctoria/doctoria-rally-ai",
		"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 doctoria/doctoria-rally-ai with Docker Model Runner:
```
docker model run hf.co/doctoria/doctoria-rally-ai
```

doctoria-rally-ai 🩺

Fine-tuned medical Vision-Language Model for SahhaAI — offline, private wound-care & medical VQA for disconnected clinics in Morocco.

Base: HuggingFaceTB/SmolVLM-256M-Instruct (SmolVLM)
Method: LoRA fine-tune (PyTorch + PEFT, trained locally on Apple Silicon / MPS)
Data: VQA-RAD (radiology VQA, CC0)
Runs: in-browser (WebGPU via transformers.js) and locally — data stays on device

Benchmark (VQA-RAD test)

Closed-ended accuracy: 0.439
Open-ended token-F1: 0.228
Speed: 19.0 tok/s on Apple Silicon (MPS)
See repo finetune/BENCHMARK.md for base-vs-fine-tuned analysis.

Use

from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image
m = AutoModelForImageTextToText.from_pretrained("doctoria/doctoria-rally-ai")
p = AutoProcessor.from_pretrained("doctoria/doctoria-rally-ai")
msgs = [{"role":"user","content":[{"type":"image"},{"type":"text","text":"Assess this wound."}]}]
text = p.apply_chat_template(msgs, add_generation_prompt=True)
inp = p(text=text, images=[[Image.open("wound.jpg")]], return_tensors="pt")
print(p.batch_decode(m.generate(**inp, max_new_tokens=128), skip_special_tokens=True)[0])

⚠ Decision support, not a diagnosis. A trained health worker stays in the loop.

— Author: Jad Tounsi El Azzouzi · part of SahhaAI · Apache-2.0

Downloads last month: 2

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for doctoria/doctoria-rally-ai

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Quantized

HuggingFaceTB/SmolVLM-256M-Instruct

Adapter

(36)

this model

doctoria
/

doctoria-rally-ai

doctoria-rally-ai 🩺

Benchmark (VQA-RAD test)

Use

Model tree for doctoria/doctoria-rally-ai

Dataset used to train doctoria/doctoria-rally-ai