Instructions to use blascotobasco/Qwen3.5-16B-Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blascotobasco/Qwen3.5-16B-Test with Transformers:

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

pipe = pipeline("image-text-to-text", model="blascotobasco/Qwen3.5-16B-Test")
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("blascotobasco/Qwen3.5-16B-Test")
model = AutoModelForMultimodalLM.from_pretrained("blascotobasco/Qwen3.5-16B-Test", 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 blascotobasco/Qwen3.5-16B-Test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "blascotobasco/Qwen3.5-16B-Test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blascotobasco/Qwen3.5-16B-Test",
		"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/blascotobasco/Qwen3.5-16B-Test

SGLang

How to use blascotobasco/Qwen3.5-16B-Test 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 "blascotobasco/Qwen3.5-16B-Test" \
    --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": "blascotobasco/Qwen3.5-16B-Test",
		"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 "blascotobasco/Qwen3.5-16B-Test" \
        --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": "blascotobasco/Qwen3.5-16B-Test",
		"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 blascotobasco/Qwen3.5-16B-Test with Docker Model Runner:
```
docker model run hf.co/blascotobasco/Qwen3.5-16B-Test
```

Qwen3.5-16B-Dense (Structural Expansion)

⚠️ Status: Architectural Expansion / Untrained Weights

This model is a dense structural expansion of Qwen3.5-9B-Instruct, scaled to 16.1B parameters. It was created using structural mitosis to increase the model's depth and breadth, providing a larger "cognitive canvas" for downstream training.

Note: As this is a structural expansion, the new parameters have not yet been calibrated. The model will require a "Repair" SFT or Continued Pre-Training (CPT) phase to utilize its expanded capacity. Vision capability is preserved, however the model will need training to utilise it effectively.

🛠 Architecture & Expansion Strategy

The expansion targets the inherent limitations of sub-10B models—specifically knowledge density and reasoning stability—by providing additional parameter headroom.

Base Model: Qwen/Qwen3.5-9B-Instruct
Expanded Parameters: ~16.1B
Methodology: Structural Mitosis
- Layer Duplication: High-importance layers were identified and duplicated to extend transformer depth.
- SVD Noise Injection: Singular Value Decomposition (SVD) based noise was injected into the duplicated weights to break symmetry and induce divergence, preventing "identity-mapping" stalls during early training.

Why 16B?

The 16B parameter count represents a strategic "sweet spot" for modern hardware. It offers a significant increase in total neurons and associative memory over the 9B base, while remaining highly performant on consumer-grade GPUs (e.g., RTX 3090/4090/5080) when quantized to 4-bit or 8-bit.

🚀 Call to Action: Training & Calibration

This model is released as a base for researchers and hobbyists interested in high-density dense models. The additional ~7.1B parameters are currently "blank" capacity ready to be filled with specialized knowledge.

Recommended Training Path:

Symmetry Breaking (Calibration): A short run on ~2-5B tokens of high-diversity data using a very low learning rate (1e-6) to allow the SVD-diverged layers to settle into functional roles.
Knowledge Distillation: Fine-tuning on high-reasoning datasets (such as Opus-distilled sets) to take advantage of the expanded FFN capacity.
DPO/PPO: Final alignment to stabilize the increased depth and prevent coherence drift during long-context generation.

📜 Credits

Base Architecture: Qwen/Qwen3.5-9B-Instruct
Expansion Framework: Self Designed "Structural Mitosis" Framework

Downloads last month: 20

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for blascotobasco/Qwen3.5-16B-Test

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Qwen/Qwen3.5-9B-Base

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Qwen/Qwen3.5-9B

Finetuned

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