Instructions to use WangSenQWQ/Senova-4B-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WangSenQWQ/Senova-4B-v4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="WangSenQWQ/Senova-4B-v4")
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("WangSenQWQ/Senova-4B-v4")
model = AutoModelForMultimodalLM.from_pretrained("WangSenQWQ/Senova-4B-v4", 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 WangSenQWQ/Senova-4B-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WangSenQWQ/Senova-4B-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WangSenQWQ/Senova-4B-v4",
		"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/WangSenQWQ/Senova-4B-v4

SGLang

How to use WangSenQWQ/Senova-4B-v4 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 "WangSenQWQ/Senova-4B-v4" \
    --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": "WangSenQWQ/Senova-4B-v4",
		"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 "WangSenQWQ/Senova-4B-v4" \
        --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": "WangSenQWQ/Senova-4B-v4",
		"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 WangSenQWQ/Senova-4B-v4 with Docker Model Runner:
```
docker model run hf.co/WangSenQWQ/Senova-4B-v4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Senova-4B-v4

Senova-4B-v4 is a full-parameter option-set soft-label fine-tune of Qwen/Qwen3.5-4B on 5,181,618 examples for 3 epochs. This release is the final checkpoint, step 1530. Model weights are licensed under Apache-2.0; see LICENSE and NOTICE. Dataset rights are not granted by the model license.

Public Decision Index 0.3 index: 67.02, complete=true; 140,178/140,178 scoreable requests successful. The scored run uses Read Twice and thinking off.

Inference repeats the complete rendered question input twice, separated by a blank line, inside one user message, then reads one next-token distribution. It does not make two model calls. Thinking stays off. The same Read Twice setting is required for private evaluation. The Senova-4B-v3 inference server can load these weights.

The weights have been modified by soft-label training and are not an unmodified Qwen release. This score is a public benchmark result. The private Full score is determined by the leaderboard maintainers.

Downloads last month: 29

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for WangSenQWQ/Senova-4B-v4

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

(1240)

this model

Quantizations

1 model