Instructions to use unsloth/Qwen3-VL-2B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/Qwen3-VL-2B-Thinking with Transformers:

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

pipe = pipeline("image-text-to-text", model="unsloth/Qwen3-VL-2B-Thinking")
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("unsloth/Qwen3-VL-2B-Thinking")
model = AutoModelForImageTextToText.from_pretrained("unsloth/Qwen3-VL-2B-Thinking")
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 unsloth/Qwen3-VL-2B-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/Qwen3-VL-2B-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/Qwen3-VL-2B-Thinking",
		"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/unsloth/Qwen3-VL-2B-Thinking

SGLang

How to use unsloth/Qwen3-VL-2B-Thinking 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 "unsloth/Qwen3-VL-2B-Thinking" \
    --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": "unsloth/Qwen3-VL-2B-Thinking",
		"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 "unsloth/Qwen3-VL-2B-Thinking" \
        --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": "unsloth/Qwen3-VL-2B-Thinking",
		"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 unsloth/Qwen3-VL-2B-Thinking 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 unsloth/Qwen3-VL-2B-Thinking 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 unsloth/Qwen3-VL-2B-Thinking to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/Qwen3-VL-2B-Thinking to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/Qwen3-VL-2B-Thinking",
    max_seq_length=2048,
)

Docker Model Runner
How to use unsloth/Qwen3-VL-2B-Thinking with Docker Model Runner:
```
docker model run hf.co/unsloth/Qwen3-VL-2B-Thinking
```

danielhanchen commited on Oct 31, 2025

Commit

fbb92ed

verified ·

1 Parent(s): ee8fc3f

Update chat_template.jinja

Browse files

Files changed (1) hide show

chat_template.jinja +13 -2

chat_template.jinja CHANGED Viewed

@@ -1,3 +1,4 @@
 {%- set image_count = namespace(value=0) %}
 {%- set video_count = namespace(value=0) %}
 {%- macro render_content(content, do_vision_count) %}
@@ -60,8 +61,17 @@
             {%- set reasoning_content = message.reasoning_content %}
         {%- else %}
             {%- if '</think>' in content %}
-                {%- set reasoning_content = ((content.split('</think>')|first).rstrip('\n').split('<think>')|last).lstrip('\n') %}
-                {%- set content = (content.split('</think>')|last).lstrip('\n') %}
             {%- endif %}
         {%- endif %}
         {%- if loop.index0 > ns.last_query_index %}
@@ -108,3 +118,4 @@
 {%- if add_generation_prompt %}
     {{- '<|im_start|>assistant\n<think>\n' }}
 {%- endif %}

+{# Unsloth template fixes #}
 {%- set image_count = namespace(value=0) %}
 {%- set video_count = namespace(value=0) %}
 {%- macro render_content(content, do_vision_count) %}
             {%- set reasoning_content = message.reasoning_content %}
         {%- else %}
             {%- if '</think>' in content %}
+                {# Unsloth template fixes - must change to for loop since llama.cpp will error out if not #}
+                {%- set parts = content.split('</think>') %}
+                {%- for part in parts %}
+                    {%- if loop.index0 == 0 -%}
+                        {%- set reasoning_content = part.rstrip('\n') %}
+                        {%- set reasoning_content = (reasoning_content.split('<think>')|last) %}
+                        {%- set reasoning_content = reasoning_content.lstrip('\n') -%}
+                    {%- else -%}
+                        {%- set content = part.lstrip('\n') %}
+                    {%- endif %}
+                {%- endfor %}
             {%- endif %}
         {%- endif %}
         {%- if loop.index0 > ns.last_query_index %}
 {%- if add_generation_prompt %}
     {{- '<|im_start|>assistant\n<think>\n' }}
 {%- endif %}
+{# Copyright 2025-present Unsloth. Apache 2.0 License. #}