---
language:
- en
- fr
- de
- es
- it
- pl
license: apache-2.0
tags:
- vision-language-model
- multimodal
- visual-question-answering
- image-captioning
- vlm
base_model:
- OpenGVLab/InternViT-300M-448px-V2_5
- PleIAs/Baguettotron
---

# baguettotron-internvit-alignment

**Baguettotron-VLM** is an open, fully-reproducible, multilingual Vision-Language
Model in the **sub-1B parameter** class. It extends
[PleIAs/Baguettotron](https://huggingface.co/PleIAs/Baguettotron) — a 321M
text-only reasoning SLM — with visual capabilities via the
[InternViT-300M-448px-V2.5](https://huggingface.co/OpenGVLab/InternViT-300M-448px-V2_5)
vision encoder and a lightweight MLP projector, for a total of ~628M parameters. It
inherits six European languages (EN, FR, DE, ES, IT, PL) from the Baguettotron backbone.

Two checkpoints are published:

- [baguettotron-internvit-alignment](https://huggingface.co/andreagemelli/baguettotron-internvit-alignment)
  — the projector-only warmup. It describes images, and nothing more.
- [baguettotron-vision-vqa](https://huggingface.co/andreagemelli/baguettotron-vision-vqa)
  — instruction-tuned on top of it. It goes past plain description and follows visual
  instructions, so prefer it for a richer chat experience.

Apache 2.0.
Source: [github.com/andreagemelli/baguettotron-vlm](https://github.com/andreagemelli/baguettotron-vlm) ·
Write-up: [andreagemelli.me/posts/baguettotron-vlm](https://andreagemelli.me/posts/baguettotron-vlm/)


> **Alignment checkpoint.** Only the MLP projector was trained (on LLaVA-CC3M-Pretrain-595K) — the ViT and the Baguettotron LLM are unmodified base weights. It writes short captions and nothing more. Published so the alignment stage can be reproduced; for actual use take [baguettotron-vision-vqa](https://huggingface.co/andreagemelli/baguettotron-vision-vqa).

## Architecture

```
Image (448×448)
  → InternViT-300M-448px-V2.5 (304M, frozen) → 1024 tokens × 1024d
  → Pixel unshuffle (factor=2)                → 256 tokens × 4096d
  → MLP projector (2-layer, ~2.7M)            → 256 tokens × 576d
  → Interleave with text tokens
  → Baguettotron (321M, Llama arch, 80L, h=576)

Total: ~628M parameters
```

## Usage

```bash
pip install "transformers>=4.56,<5" torch pillow timm einops accelerate
```

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

model = AutoModelForImageTextToText.from_pretrained(
    "andreagemelli/baguettotron-internvit-alignment",
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto",  # also tested on Apple Silicon (mps) and CPU
)
processor = AutoProcessor.from_pretrained(
    "andreagemelli/baguettotron-internvit-alignment",
    trust_remote_code=True,
)

image = Image.open("photo.jpg").convert("RGB")
inputs = processor(
    messages=[{"role": "user", "content": "<image>\nDescribe the image concisely."}],
    image=image,
)
inputs = {k: v.to(model.device) for k, v in inputs.items() if v is not None}

print(model.chat(**inputs))
```

### Example output

Greedy, prompt `Describe the image concisely.` — verbatim output. Images from [COCO](https://cocodataset.org) val2017.

| | output |
|---|---|
| <img src="examples/cats.jpg" width="180"> | `a cat is sleeping on the couch` |
| <img src="examples/bear.jpg" width="180"> | `the bear is a good friend.` |
| <img src="examples/stop.jpg" width="180"> | `a sign for a stop sign` |
| <img src="examples/bus.jpg" width="180"> | `the bus is a red double - decoration` |

### Chat template

Trained on short image captions with no `<think>` traces. The processor emits a bare assistant prefix (`<|im_start|>assistant\n`) and the model completes the caption directly. Keep prompts simple ("Describe the image").

## Limitations

- **Resolution ceiling.** One 448×448 crop → 256 visual tokens puts document text at
  roughly 2–4 px/char. OCR, charts and documents are out of reach by architecture, not
  by budget. Neither published checkpoint reads text in an image.
- **Hallucinations**, especially on fine-grained or text-heavy questions.
- **Multilingual capability is inherited, not verified.** The backbone covers six
  languages; the VLM was never evaluated on non-English benchmarks.

> Tested against `transformers` 4.57. Newer major versions may need adjustments.

**Contributions and suggestions are very welcome** — issues, PRs, and ideas for
better data mixes, training recipes, or evaluation setups are all appreciated.
Open an issue or PR on the
[GitHub repo](https://github.com/andreagemelli/baguettotron-vlm).


## Citation

If you use or extend Baguettotron-VLM in your research, please cite it:

```bibtex
@misc{gemelli2026baguettotronvlm,
  title        = {Baguettotron-VLM: An Open, Reproducible, Multilingual Sub-1B Vision-Language Model},
  author       = {Gemelli, Andrea},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/andreagemelli/baguettotron-vision-vqa}},
  note         = {Code: \url{https://github.com/andreagemelli/baguettotron-vlm}}
}
```


## License

Apache 2.0 — see the [GitHub repo](https://github.com/andreagemelli/baguettotron-vlm).