Instructions to use zss01/BiPS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zss01/BiPS with Transformers:

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

pipe = pipeline("image-text-to-text", model="zss01/BiPS")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("zss01/BiPS")
model = AutoModelForImageTextToText.from_pretrained("zss01/BiPS")
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

vLLM

How to use zss01/BiPS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zss01/BiPS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zss01/BiPS",
		"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/zss01/BiPS

SGLang

How to use zss01/BiPS 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 "zss01/BiPS" \
    --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": "zss01/BiPS",
		"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 "zss01/BiPS" \
        --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": "zss01/BiPS",
		"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 zss01/BiPS with Docker Model Runner:
```
docker model run hf.co/zss01/BiPS
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BiPS — Bi-directional Perceptual Shaping for Multimodal Reasoning

This model card describes BiPS (Bi-directional Perceptual Shaping), a training-time framework proposed in “See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning” [CVPR 2026].

Paper: https://arxiv.org/abs/2512.22120
Code: https://github.com/zss02/BiPS

What is BiPS?

Many VLMs fail on multimodal reasoning because they look at the wrong visual evidence (especially for charts, thin lines, intersections, and small regions). BiPS improves question-conditioned visual grounding by turning “where-to-look” supervision into training signals—without requiring extra tools at inference time.

Key idea

BiPS trains a VLM with two complementary view transformations:

Evidence-Preserving View: keep only the visual evidence needed to answer, reduce distractions.
→ enforce consistency between predictions from the original image and the preserved view.
Evidence-Ablated View: remove the key evidence so the image no longer supports the answer.
→ enforce separation so the model cannot rely on shortcuts.

These constraints are typically implemented with KL-based objectives and can be integrated into GRPO training.

Why it matters

Better fine-grained evidence alignment
Less “guessing” from language priors
No additional inference overhead (views are used only during training)

How to use

BiPS is mainly a training recipe. To apply it:

Follow the official repo to set up dependencies and scripts.
Train your base VLM with BiPS-generated preserve/ablate views.
Use the resulting checkpoint as a standard VLM at inference time (no extra steps).

Citation

@article{zhang2025bips,
  title={See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning},
  author={Zhang, Shuoshuo and Zhang, Yizhen and Fu, Jingjing and Song, Lei and Bian, Jiang and Yang, Yujiu and Wang, Rui},
  journal={arXiv preprint arXiv:2512.22120},
  year={2025}
}