Instructions to use VLR-CVC/CVC-Qwen3.5-2B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VLR-CVC/CVC-Qwen3.5-2B-SFT with Transformers:

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

pipe = pipeline("image-text-to-text", model="VLR-CVC/CVC-Qwen3.5-2B-SFT")
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("VLR-CVC/CVC-Qwen3.5-2B-SFT")
model = AutoModelForMultimodalLM.from_pretrained("VLR-CVC/CVC-Qwen3.5-2B-SFT", 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 VLR-CVC/CVC-Qwen3.5-2B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VLR-CVC/CVC-Qwen3.5-2B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VLR-CVC/CVC-Qwen3.5-2B-SFT",
		"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/VLR-CVC/CVC-Qwen3.5-2B-SFT

SGLang

How to use VLR-CVC/CVC-Qwen3.5-2B-SFT 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 "VLR-CVC/CVC-Qwen3.5-2B-SFT" \
    --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": "VLR-CVC/CVC-Qwen3.5-2B-SFT",
		"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 "VLR-CVC/CVC-Qwen3.5-2B-SFT" \
        --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": "VLR-CVC/CVC-Qwen3.5-2B-SFT",
		"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 VLR-CVC/CVC-Qwen3.5-2B-SFT with Docker Model Runner:
```
docker model run hf.co/VLR-CVC/CVC-Qwen3.5-2B-SFT
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

CVC-Qwen3.5-2B-Instruct

Vision-language instruct model finetuned from Qwen/Qwen3.5-2B-Base. For more info see the 35B-A3B model card.

Dense, 2B parameters: 24 decoder layers plus a 24-layer vision encoder. Weights are bf16.

Training

Two supervised finetuning stages, both at sequence length 16384 on 64 nodes of JUPITER (4x GH200 per node):

Instruct SFT — one pass over the LLaVA-OneVision-1.5-Instruct and Nemotron-VLM-V2 blend, 1139 steps / 16.7B tokens.
Decay anneal — a further 156 steps / 2.3B tokens over the same sources reweighted towards document and OCR data, with the learning rate decayed to zero.

Learning rate 1e-5 for the decoder and projector, 1e-6 for the vision encoder, 100 warmup steps. Images are capped at 1024px on the long side.

Notes

Uses the Qwen3.5 chat template. Unlike the upstream instruct template, this one renders <think> blocks on every assistant turn rather than only the last, matching how the model was trained.
eos_token is <|im_end|>, and generation_config.json stops on both <|im_end|> (248046) and <|endoftext|> (248044).

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for VLR-CVC/CVC-Qwen3.5-2B-SFT

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

(128)

this model