Instructions to use Junlaii/Nicesse-RARA-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Junlaii/Nicesse-RARA-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Junlaii/Nicesse-RARA-3B")
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("Junlaii/Nicesse-RARA-3B")
model = AutoModelForMultimodalLM.from_pretrained("Junlaii/Nicesse-RARA-3B", 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 Junlaii/Nicesse-RARA-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Junlaii/Nicesse-RARA-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Junlaii/Nicesse-RARA-3B",
		"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/Junlaii/Nicesse-RARA-3B

SGLang

How to use Junlaii/Nicesse-RARA-3B 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 "Junlaii/Nicesse-RARA-3B" \
    --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": "Junlaii/Nicesse-RARA-3B",
		"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 "Junlaii/Nicesse-RARA-3B" \
        --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": "Junlaii/Nicesse-RARA-3B",
		"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 Junlaii/Nicesse-RARA-3B with Docker Model Runner:
```
docker model run hf.co/Junlaii/Nicesse-RARA-3B
```

Nicesse-RARA-3B / README.md

Junlaii

Rename release to Nicesse-RARA-3B and refresh integrated model card

176f2a6 verified 21 days ago

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metadata

library_name: transformers
pipeline_tag: image-text-to-text
license: other
tags:
  - chart-vqa
  - visual-question-answering
  - qwen2.5-vl
  - multimodal
  - numerical-reasoning

Nicesse-RARA-3B

Nicesse-RARA-3B is a complete 3B chart visual question answering model in the Qwen2.5-VL family. It is trained with Reweighted Answer-Restricted Adaptation (RARA), which combines challenge-aware example exposure with answer-focused supervision for numerical and comparative chart reasoning. The repository contains the complete model checkpoint and the matching processor configuration for direct use with transformers.

Model Details

Item	Value
Model family	Qwen2.5-VL-3B
Parameter scale	3B
Architecture	Vision-language generation model
Training objective	Challenge-aware sampling with answer-restricted supervision
Primary use	Chart visual question answering and numerical reasoning
Inputs	Chart image and natural-language question
Outputs	Short free-form answer

Benchmark Results

The table reports publicly available results on four chart VQA benchmarks. Scores are dataset-level accuracy in percent; higher is better. ChartQAPro additionally has a strict-audit score of 22.28.

Method	ChartQA	PlotQA	EvoChart	ChartQAPro	Average
Qwen2.5-VL-3B (direct prompting)	82.00	80.50	48.72	25.70	59.23
Qwen2.5-VL-3B (CoT)	73.12	52.72	29.60	15.80	42.81
ChartGemma	76.44	33.28	36.96	10.93	39.40
SketchVL-3B	77.20	48.32	47.28	44.15	54.24
Qwen2.5-VL-3B SFT	83.08	74.18	46.08	23.56	56.73
Qwen2.5-VL-3B DPO	75.42	53.86	34.80	15.95	45.01
Qwen2.5-VL-3B (A+F+L)	76.72	56.22	38.88	17.55	47.34
Qwen2.5-VL-3B (A+F+L+Tasks)	81.80	76.24	51.68	27.66	59.35
Chart-RVR-3B	84.56	78.68	53.36	28.38	61.25
Chart-RVR-3B-Hard	85.76	77.90	54.24	28.64	61.64
Qwen2.5-VL + Nicesse-RARA-3B	83.76	89.42	54.32	37.94	66.36

The Nicesse-RARA-3B configuration was selected using a frozen development split. It was then evaluated once on four frozen benchmark manifests. The result is strongest on PlotQA among the listed comparable public 3B models; its ChartQAPro result follows the published-code-compatible evaluation protocol.

Quick Start

from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration

model_id = "Junlaii/Nicesse-RARA-3B"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "path/to/chart.png"},
        {"type": "text", "text": "What is the value of the blue bar in 2010?"},
    ],
}]
prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[prompt], images=["path/to/chart.png"], return_tensors="pt").to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=768)
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])

Intended Use and Limitations

Nicesse-RARA-3B is intended for research on chart understanding, visual question answering, and numerical reasoning. It can make visual-reading, arithmetic, formatting, and ambiguous-question errors. Benchmark results do not establish reliability for high-stakes decisions; validate outputs independently in any downstream deployment.

License and Attribution

This model is distributed under the included Qwen Research License Agreement. It is improved using Qwen. Please follow the license terms and cite the relevant Qwen2.5-VL and RARA work when appropriate.