Instructions to use Svngoku/Qwen3-VL-TimeTravel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Svngoku/Qwen3-VL-TimeTravel with Transformers:

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

pipe = pipeline("image-text-to-text", model="Svngoku/Qwen3-VL-TimeTravel")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Svngoku/Qwen3-VL-TimeTravel")
model = AutoModelForImageTextToText.from_pretrained("Svngoku/Qwen3-VL-TimeTravel")
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

vLLM

How to use Svngoku/Qwen3-VL-TimeTravel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Svngoku/Qwen3-VL-TimeTravel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Svngoku/Qwen3-VL-TimeTravel",
		"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/Svngoku/Qwen3-VL-TimeTravel

SGLang

How to use Svngoku/Qwen3-VL-TimeTravel 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 "Svngoku/Qwen3-VL-TimeTravel" \
    --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": "Svngoku/Qwen3-VL-TimeTravel",
		"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 "Svngoku/Qwen3-VL-TimeTravel" \
        --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": "Svngoku/Qwen3-VL-TimeTravel",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio new

How to use Svngoku/Qwen3-VL-TimeTravel with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Svngoku/Qwen3-VL-TimeTravel to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Svngoku/Qwen3-VL-TimeTravel to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Svngoku/Qwen3-VL-TimeTravel to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Svngoku/Qwen3-VL-TimeTravel",
    max_seq_length=2048,
)

Docker Model Runner
How to use Svngoku/Qwen3-VL-TimeTravel with Docker Model Runner:
```
docker model run hf.co/Svngoku/Qwen3-VL-TimeTravel
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-VL-TimeTravel Fine-tuned Model

This model is a fine-tuned version of the Qwen3-VL-8B-Instruct model using the Unsloth library on the MBZUAI/TimeTravel dataset.

Model Details

Base Model: Qwen3-VL-8B-Instruct-unsloth-bnb-4bit
Fine-tuning Library: Unsloth
Dataset: MBZUAI/TimeTravel
Task: Image description generation for historical artifacts.

Training

The model was trained for 4 epochs with a batch size of 8 (4 per device with 8 gradient accumulation steps). LoRA adapters were used for parameter efficient finetuning, targeting both vision and language layers, as well as attention and MLP modules.

Dataset

TimeTravel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts

Sara Ghaboura ^* Ketan More ^* Retish Thawkar Wafa Alghallabi Omkar Thawakar
Fahad Shahbaz Khan Hisham Cholakkal Salman Khan Rao M. Anwer
^{*Equal Contribution}

🏛 TimeTravel Taxonomy and Diversity

TimeTravel Taxonomy maps artifacts from 10 civilizations, 266 cultures, and 10k+ verified samples for AI-driven historical analysis.

tax

🌟 Key Features

TimeTravel is the first large-scale, open-source benchmark designed to evaluate Large Multimodal Models (LMMs) on historical and cultural artifacts. It covers:

266 Cultural Groups across 10 Historical Regions
10,000+ Expert-Verified Artifact Samples
Multimodal Image-Text Dataset for AI-driven historical research
A publicly available dataset and evaluation framework to advance AI applications in history and archaeology.

🔄 TimeTravel Creation Pipeline

The TimeTravel dataset follows a structured pipeline to ensure the accuracy, completeness, and contextual richness of historical artifacts.

pipeline

Our approach consists of four key phases:

Data Selection: Curated 10,250 artifacts from museum collections, spanning 266 cultural groups, with expert validation to ensure historical accuracy and diversity.
Data Cleaning: Addressed missing or incomplete metadata (titles, dates, iconography) by cross-referencing museum archives and academic sources, ensuring data consistency.
Generation & Verification: Used GPT-4o to generate context-aware descriptions, which were refined and validated by historians and archaeologists for authenticity.
Data Aggregation: Standardized and structured dataset into image-text pairs, making it a valuable resource for AI-driven historical analysis and cultural heritage research.

🏆 TimeTravel Evaluation

The table below showcases the performance comparison of various closed and open-source models on our proposed TimeTravel benchmark.

Model BLEU METEOR ROUGE-L SPICE BERTScore LLM-Judge

GPT-4o-0806 0.1758🏅 0.2439 0.1230🏅 0.1035🏅 0.8349🏅 0.3013🏅

Gemini-2.0-Flash 0.1072 0.2456 0.0884 0.0919 0.8127 0.2630

Gemini-1.5-Pro 0.1067 0.2406 0.0848 0.0901 0.8172 0.2276

GPT-4o-mini-0718 0.1369 0.2658🏅 0.1027 0.1001 0.8283 0.2492

Llama-3.2-Vision-Inst 0.1161 0.2072 0.1027 0.0648 0.8111 0.1255

Qwen-2.5-VL 0.1155 0.2648 0.0887 0.1002 0.8198 0.1792

Llava-Next 0.1118 0.2340 0.0961 0.0799 0.8246 0.1161

Model	BLEU	METEOR	ROUGE-L	SPICE	BERTScore	LLM-Judge
GPT-4o-0806	0.1758🏅	0.2439	0.1230🏅	0.1035🏅	0.8349🏅	0.3013🏅
Gemini-2.0-Flash	0.1072	0.2456	0.0884	0.0919	0.8127	0.2630
Gemini-1.5-Pro	0.1067	0.2406	0.0848	0.0901	0.8172	0.2276
GPT-4o-mini-0718	0.1369	0.2658🏅	0.1027	0.1001	0.8283	0.2492
Llama-3.2-Vision-Inst	0.1161	0.2072	0.1027	0.0648	0.8111	0.1255
Qwen-2.5-VL	0.1155	0.2648	0.0887	0.1002	0.8198	0.1792
Llava-Next	0.1118	0.2340	0.0961	0.0799	0.8246	0.1161

Model India Roman Emp. China British Isles Iran Iraq Japan Cent. America Greece Egypt

GPT-4o-0806 0.2491🏅 0.4463🏅 0.2491🏅 0.1899🏅 0.3522🏅 0.3545🏅 0.2228🏅 0.3144🏅 0.2757🏅 0.3649🏅

Gemini-2.0-Flash 0.1859 0.3358 0.2059 0.1556 0.3376 0.3071 0.2000 0.2677 0.2582 0.3602

Gemini-1.5-Pro 0.1118 0.2632 0.2139 0.1545 0.3320 0.2587 0.1871 0.2708 0.2088 0.2908

GPT-4o-mini-0718 0.2311 0.3612 0.2207 0.1866 0.2991 0.2632 0.2087 0.3195 0.2101 0.2501

Llama-3.2-Vision-Inst 0.0744 0.1450 0.1227 0.0777 0.2000 0.1155 0.1075 0.1553 0.1351 0.1201

Qwen-2.5-VL 0.0888 0.1578 0.1192 0.1713 0.2515 0.1576 0.1771 0.1442 0.1442 0.2660

Llava-Next 0.0788 0.0961 0.1455 0.1091 0.1464 0.1194 0.1353 0.1917 0.1111 0.0709

Model	India	Roman Emp.	China	British Isles	Iran	Iraq	Japan	Cent. America	Greece	Egypt
GPT-4o-0806	0.2491🏅	0.4463🏅	0.2491🏅	0.1899🏅	0.3522🏅	0.3545🏅	0.2228🏅	0.3144🏅	0.2757🏅	0.3649🏅
Gemini-2.0-Flash	0.1859	0.3358	0.2059	0.1556	0.3376	0.3071	0.2000	0.2677	0.2582	0.3602
Gemini-1.5-Pro	0.1118	0.2632	0.2139	0.1545	0.3320	0.2587	0.1871	0.2708	0.2088	0.2908
GPT-4o-mini-0718	0.2311	0.3612	0.2207	0.1866	0.2991	0.2632	0.2087	0.3195	0.2101	0.2501
Llama-3.2-Vision-Inst	0.0744	0.1450	0.1227	0.0777	0.2000	0.1155	0.1075	0.1553	0.1351	0.1201
Qwen-2.5-VL	0.0888	0.1578	0.1192	0.1713	0.2515	0.1576	0.1771	0.1442	0.1442	0.2660
Llava-Next	0.0788	0.0961	0.1455	0.1091	0.1464	0.1194	0.1353	0.1917	0.1111	0.0709

🖼 TimeTravel Examples

The figure illustrates the cultural and material diversity of the TimeTravel dataset.

tax

📂 TimeTravle Dataset Schema

📷 Image (image)
🔹 id (string)
📅 Production date (string)
📍 Find spot (string)
🔸 Materials (string)
🛠 Technique (string)
📝 Inscription (string)
🎭 Subjects (string)
📛 Assoc name (string)
🏛 Culture (string)
📂 Section (string)
🌍 Place (string)
📝 description (string)

📚 Citation

If you use TimeTravle dataset in your research, please consider citing:

@misc{ghaboura2025timetravelcomprehensivebenchmark,
      title={Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts}, 
      author={Sara Ghaboura and Ketan More and Ritesh Thawkar and Wafa Alghallabi and Omkar Thawakar and Fahad Shahbaz Khan and Hisham Cholakkal and Salman Khan and Rao Muhammad Anwer},
      year={2025},
      eprint={2502.14865},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.14865}, 
}