Instructions to use inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct")
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("inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct", 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 inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct",
		"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/inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct

SGLang

How to use inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct 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 "inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct" \
    --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": "inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct",
		"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 "inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct" \
        --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": "inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct",
		"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 inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct with Docker Model Runner:
```
docker model run hf.co/inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct
```

Qwen3-VL-1.0B-A0.4B-Instruct / README.md

kylesayrs

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	---
	license: mit
	base_model:
	- Qwen/Qwen3-VL-30B-A3B-Instruct
	library_name: transformers
	---

	# Qwen3-VL-1.0B-A0.4B-Instruct

	This is a tiny version of [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct) created for testing and development.

	## Model Details

	- Base Model: Qwen/Qwen3-VL-30B-A3B-Instruct
	- Architecture: qwen3_vl_moe
	- Total Parameters: 1.434B
	- Activated Parameters: ~0.4B (8 of 128 experts active per token)

	## Configuration Changes

	The following parameters were reduced from the original model:

	\| Parameter \| Original \| Tiny \|
	\|-----------\|----------\|------\|
	\| `text_config.num_hidden_layers` \| 48 \| 1 \|
	\| `vision_config.depth` \| 27 \| 4 \|
	\| `vision_config.deepstack_visual_indexes` \| [8, 16, 24] \| [1, 2, 3] \|
	\| `text_config.hidden_size` \| 2048 \| 2048 (unchanged) \|
	\| `text_config.num_local_experts` \| 128 \| 128 (unchanged) \|
	\| `text_config.num_experts_per_tok` \| 8 \| 8 (unchanged) \|
	\| `text_config.moe_intermediate_size` \| 768 \| 768 (unchanged) \|
	\| `text_config.num_attention_heads` \| 32 \| 32 (unchanged) \|
	\| `text_config.num_key_value_heads` \| 4 \| 4 (unchanged) \|
	\| `vision_config.hidden_size` \| 1152 \| 1152 (unchanged) \|

	## Checkpoint Structure

	The model is saved as a single `model.safetensors` file (89 tensors). The checkpoint key structure matches the original `Qwen/Qwen3-VL-30B-A3B-Instruct` exactly.

	## Usage

	```python
	from transformers import Qwen3VLMoeForConditionalGeneration, AutoTokenizer

	model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
	"inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct",
	device_map="auto",
	)
	tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Qwen3-VL-1.0B-A0.4B-Instruct")

	input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
	output = model.generate(input_ids, max_new_tokens=20)
	print(tokenizer.decode(output[0]))
	```

	Note: `AutoModelForCausalLM` does not support this model type. Use `Qwen3VLMoeForConditionalGeneration` or `AutoModelForImageTextToText` directly.

	## Creation Process

	This model was created using the llm-compressor `create-tiny-model` claude skill.

	1. Config fetched from `Qwen/Qwen3-VL-30B-A3B-Instruct` without downloading weights (`skip_weights_download`)
	2. Text depth reduced from 48 → 1 layers; vision depth reduced from 27 → 4 blocks
	3. All parameters randomly initialized with `initializer_range=0.02` (matching the base model)
	4. MoE expert parameters (`gate_up_proj`, `down_proj`, `gate.weight`) explicitly initialized since they are bare `nn.Parameter` tensors, not `nn.Linear` modules
	5. Fine-tuned on a toy text dataset (internet copypastas) until perplexity ≤ 3.0

	## Validation

	```
	Success: perplexity=1.0001 <= 10.0
	Generated: According to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little
	```

	## Notes

	- This is a text-only fine-tuned model. The vision encoder weights are randomly initialized and not fine-tuned; the model is intended for text-based testing only.
	- The MoE routing architecture (128 experts, 8 active per token) is preserved in full to match the original model's structural properties.
	- Fine-tuning converged in ~160 steps with learning rate 1e-4.