README.md

---
library_name: transformers
license: apache-2.0
language:
  - en
pipeline_tag: image-text-to-text
tags:
  - panovlm
  - fastvit
  - vision-language
  - linear-attention
---

# PanoVLM-500M

PanoVLM is a linear-attention vision-language model: a FastViT-HD vision encoder (timm) feeding a PanoLM linear-attention causal LM through a lightweight
projector.

- Type: Vision-Language (image-text-to-text) Model
- LM: PanoLM-380M
- Vision encoder: FastViT-HD (timm), NCHW input
- Projector: BitLinear (encoder dim → LM dim)
- Default image resolution: 1024×1024 (pad-resized; only the resolution is meant to be changed)

## Parameters

| Component                 | Parameters |
|---------------------------|-----------:|
| PanoLM LM                 |     ~387 M |
| FastViT-HD vision encoder |     ~123 M |
| Projector                 |       ~3 M |
| **Total**                 |  **~513 M** |

## Requirements

```text
torch==2.12.0
transformers==5.8.1
flash-linear-attention==0.5.0
timm==1.0.25
```

## Usage

Replace `<repo_id>` with the HF Hub identifier.

```python
from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image
import requests

repo_id = "<repo_id>"
model = AutoModelForImageTextToText.from_pretrained(
    repo_id, trust_remote_code=True,
).cuda()  # fla's RMSNorm uses Triton kernels that only run on CUDA tensors.
processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=True)

url = "https://llava-vl.github.io/static/images/view.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# PanoVLM's chat template wraps string content, so put the <|image|> placeholder
# inline in the message text (the processor expands it into the image tokens).
# Keep the space after <|image|>: the HF tokenizer, unlike the training tokenizer,
# does not implicitly insert one at the special-token boundary.
messages = [{"role": "user", "content": "<|image|> Is there a boat in the image?"}]
prompt = processor.tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False,
)
inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)

out = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(out[0], skip_special_tokens=True))
```