Instructions to use aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT with Transformers:

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

pipe = pipeline("image-text-to-text", model="aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT")
model = AutoModelForMultimodalLM.from_pretrained("aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT",
		"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/aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT

SGLang

How to use aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT 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 "aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT" \
    --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": "aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT",
		"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 "aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT" \
        --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": "aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT",
		"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 aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT with Docker Model Runner:
```
docker model run hf.co/aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT
```

AnomalyThink-Qwen2.5-VL-7B-SFT

Supervised-fine-tuning-only baseline (DS-MVTec 80.16 / VisA 64.78).

A Qwen2.5-VL-7B model fine-tuned for explainable industrial anomaly detection (IAD). Given a product image it produces a structured reasoning trace (<think>), a defect <location> and <type> (for anomalies), and a binary <answer>. Research artefact from the MSc thesis Reasoning-Enhanced Vision-Language Models for Explainable Industrial Anomaly Detection (TU Delft, 2026).

What this model is

This is the plain starting point of the AnomalyThink family. The base model is fine-tuned directly on 6,000 teacher-written reasoning traces. There is no reinforcement-learning stage and no self-distillation loop. Use it as the reference point that the later models are measured against.

It is not the Keep-Correct-Revise (KCR) model. KCR is the recipe where an SFT plus GRPO policy generates its own traces, which are then kept when correct, corrected by a teacher when wrong, and revised when correct but weakly grounded, after which the base model is fine-tuned on that curated result. The KCR model is released separately as aacudad/AnomalyThink-Qwen2.5-VL-7B-KCR.

Results (MMAD subsets, balanced accuracy)

Benchmark	Balanced accuracy
DS-MVTec (1,670)	80.16%
VisA (2,141)	64.78%

Evaluated under a single common harness on the MMAD DS-MVTec and VisA subsets. This is the SFT-only headline before any RL stage.

Training

Supervised fine-tuning on the AnomalyThink-6K corpus. 6,000 Gemini-2.5-Flash traces, vision encoder frozen, epoch 3 of 4.

The "AnomalyThink" reasoning traces were distilled from Gemini 2.5-Flash on Real-IAD images. Training data: aacudad/AnomalyThink (folder anomalythink_6k).

The AnomalyThink Qwen family

Model	DS-MVTec	VisA	Note
`…-7B-KCR`	82.80	72.07	thesis headline
`…-7B-SFT`	80.16	64.78	this model
`…-7B-SFT-GRPO`	82.73	70.39	this model plus GRPO
`…-7B-KCR-GRPO`	82.95	72.62	research preview, not a thesis result

Usage

import torch
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
model_id = "aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)
# Build the structured single-image IAD prompt + image, then generate.

Intended use and limitations

Research on explainable IAD. Known limitations: the model can confidently hallucinate a defect on a normal part (false positive), and GRPO-lineage variants can over-predict the "Missing Parts" type. As the public DS-MVTec/VisA images may appear in VLM pretraining, absolute numbers should be read with that caveat.

Citation

@mastersthesis{acudad2026reasoning,
  author  = {Acudad, A.},
  title   = {Reasoning-Enhanced Vision-Language Models for Explainable Industrial Anomaly Detection},
  school  = {Delft University of Technology},
  year    = {2026},
  type    = {Master's thesis},
  url     = {https://resolver.tudelft.nl/uuid:65c62420-79c0-447f-b095-7fb11d4474fc}
}

Thesis: https://resolver.tudelft.nl/uuid:65c62420-79c0-447f-b095-7fb11d4474fc. Code and evaluation files: https://github.com/aacudad/IAD-VLMs.

License

Apache-2.0 (inherits the Qwen2.5-VL-7B base). Trained on Real-IAD (cite Real-IAD separately, images are not redistributed) with traces distilled from Gemini 2.5-Flash.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for aacudad/AnomalyThink-Qwen2.5-VL-7B-SFT

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(1,269)

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AnomalyThink-Qwen2.5-VL-7B-SFT