Instructions to use chaser2/VIG-Qwen3-VL-Thinking-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chaser2/VIG-Qwen3-VL-Thinking-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="chaser2/VIG-Qwen3-VL-Thinking-8B")
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("chaser2/VIG-Qwen3-VL-Thinking-8B")
model = AutoModelForMultimodalLM.from_pretrained("chaser2/VIG-Qwen3-VL-Thinking-8B", 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 chaser2/VIG-Qwen3-VL-Thinking-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chaser2/VIG-Qwen3-VL-Thinking-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chaser2/VIG-Qwen3-VL-Thinking-8B",
		"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/chaser2/VIG-Qwen3-VL-Thinking-8B

SGLang

How to use chaser2/VIG-Qwen3-VL-Thinking-8B 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 "chaser2/VIG-Qwen3-VL-Thinking-8B" \
    --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": "chaser2/VIG-Qwen3-VL-Thinking-8B",
		"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 "chaser2/VIG-Qwen3-VL-Thinking-8B" \
        --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": "chaser2/VIG-Qwen3-VL-Thinking-8B",
		"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 chaser2/VIG-Qwen3-VL-Thinking-8B with Docker Model Runner:
```
docker model run hf.co/chaser2/VIG-Qwen3-VL-Thinking-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VIG Qwen3-VL-Thinking 8B

This is a Qwen3-VL-Thinking 8B vision-language reasoning model trained with the VIG method. It is intended for efficient visual reasoning and reproducible research.

Model Details

Architecture: Qwen3VLForConditionalGeneration
Transformers model type: qwen3_vl
Precision: bfloat16
Input: image and text
Output: generated text
Weights: complete Safetensors checkpoint

Usage

import torch
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

model_id = "chaser2/VIG-Qwen3-VL-Thinking-8B"
model = Qwen3VLForConditionalGeneration.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "path/to/image.jpg"},
        {"type": "text", "text": "Describe the image and explain your reasoning."}
    ]
}]
prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[prompt], images=["path/to/image.jpg"], return_tensors="pt", padding=True).to(model.device)
with torch.inference_mode():
    outputs = model.generate(**inputs, max_new_tokens=1024)
answer = outputs[:, inputs.input_ids.shape[1]:]
print(processor.batch_decode(answer, skip_special_tokens=True)[0])

The repository contains the complete model weights, configuration, tokenizer, processor, and generation configuration for reproducible use.

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for chaser2/VIG-Qwen3-VL-Thinking-8B

Quantizations

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