Instructions to use inference-optimization/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-Qwen3-VL-1.0B-A0.4B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("inference-optimization/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-Qwen3-VL-1.0B-A0.4B-Instruct

SGLang

How to use inference-optimization/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-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/temporary-3-layer-Qwen3-VL-1.0B-A0.4B-Instruct with Docker Model Runner:
```
docker model run hf.co/inference-optimization/temporary-3-layer-Qwen3-VL-1.0B-A0.4B-Instruct
```

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

This is a tiny version of 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

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.

Config fetched from Qwen/Qwen3-VL-30B-A3B-Instruct without downloading weights (skip_weights_download)
Text depth reduced from 48 → 1 layers; vision depth reduced from 27 → 4 blocks
All parameters randomly initialized with initializer_range=0.02 (matching the base model)
MoE expert parameters (gate_up_proj, down_proj, gate.weight) explicitly initialized since they are bare nn.Parameter tensors, not nn.Linear modules
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.

Downloads last month: -

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for inference-optimization/temporary-3-layer-Qwen3-VL-1.0B-A0.4B-Instruct

Base model

Qwen/Qwen3-VL-30B-A3B-Instruct

Finetuned

(26)

this model