Instructions to use small-models-for-glam/iconclass-vlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use small-models-for-glam/iconclass-vlm with Transformers:

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

pipe = pipeline("image-text-to-text", model="small-models-for-glam/iconclass-vlm")
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("small-models-for-glam/iconclass-vlm")
model = AutoModelForMultimodalLM.from_pretrained("small-models-for-glam/iconclass-vlm", 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 small-models-for-glam/iconclass-vlm with vLLM:

Install from pip and serve model

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

SGLang

How to use small-models-for-glam/iconclass-vlm 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 "small-models-for-glam/iconclass-vlm" \
    --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": "small-models-for-glam/iconclass-vlm",
		"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 "small-models-for-glam/iconclass-vlm" \
        --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": "small-models-for-glam/iconclass-vlm",
		"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 small-models-for-glam/iconclass-vlm with Docker Model Runner:
```
docker model run hf.co/small-models-for-glam/iconclass-vlm
```

Update README.md

by davanstrien HF Staff - opened Oct 16, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-7

Files changed (1) hide show

README.md +8 -7

README.md CHANGED Viewed

@@ -24,6 +24,8 @@ This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://hugg
 You can explore the predictions of this model using this [Space](https://huggingface.co/spaces/davanstrien/iconclass-predictions).
 ## Model Description
 This vision-language model has been fine-tuned to generate [Iconclass](https://iconclass.org/) classification codes from images. Iconclass is a comprehensive classification system for describing the content of images, particularly used in cultural heritage and art history contexts.
@@ -99,11 +101,10 @@ print(response)
 ### Training Dataset
 The model was trained on a reformatted version of the Brill Iconclass AI Test Set [biglam/brill_iconclass](https://huggingface.co/datasets/biglam/brill_iconclass).
-The dataset was reformatted into a messages format suitable for SFT training.
-Training Procedure
-<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>
 This model was trained with SFT (Supervised Fine-Tuning).
@@ -118,13 +119,13 @@ Tokenizers: 0.21.4
 ### Limitations and Biases
-The Iconclass classification system reflects biases from its creation period (1940s Netherlands)
-Certain categories, particularly those related to human classification, may contain outdated or problematic terminology
-Model performance may vary on images outside the Western art tradition due to dataset composition
 ### Citations
-Model and Training
 ```bibtex
 @misc{vonwerra2022trl,

 You can explore the predictions of this model using this [Space](https://huggingface.co/spaces/davanstrien/iconclass-predictions).
+**Note:** this model is a work in progress with the goal to see how far small models can be created to excel at this kind of specific but challenging task. As a result the base model used may change over time.
 ## Model Description
 This vision-language model has been fine-tuned to generate [Iconclass](https://iconclass.org/) classification codes from images. Iconclass is a comprehensive classification system for describing the content of images, particularly used in cultural heritage and art history contexts.
 ### Training Dataset
 The model was trained on a reformatted version of the Brill Iconclass AI Test Set [biglam/brill_iconclass](https://huggingface.co/datasets/biglam/brill_iconclass).
+The dataset was reformatted into a message format suitable for SFT training.
+### Training Procedure
 This model was trained with SFT (Supervised Fine-Tuning).
 ### Limitations and Biases
+The Iconclass classification system reflects biases from its creation period (1940s Netherlands).
+Certain categories, particularly those related to human classification, may contain outdated or problematic terminology.
+Model performance may vary on images outside the Western art tradition due to the dataset composition.
 ### Citations
+Training framework
 ```bibtex
 @misc{vonwerra2022trl,