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Running on Zero
Running on Zero
| title: Demo of Encoder-Free VLM Trained for $100 | |
| emoji: 🖼️ | |
| colorFrom: indigo | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 5.49.1 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| short_description: Ask questions about images and get answers instantly | |
| # Encoder-Free VLM Demo | |
| An **encoder-free** vision-language model: there is no pretrained vision | |
| encoder. A learned patch embedder splits a 512×512 image into 32×32 patches and | |
| projects them directly into the hidden space of | |
| [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B). The projected patch | |
| embeddings are spliced into `<|image|>` placeholder positions and the decoder | |
| generates from `inputs_embeds`. | |
| This Space serves the **best checkpoint** trained on the | |
| **DenseFusion-1M + ShareGPT4V** data mix. | |
| Upload a single image, ask a question, and the model streams a response. The | |
| demo is single-image and stateless — each turn is a fresh conversation. | |
| Inspired by the | |
| [SmolVLM-256M-Demo](https://huggingface.co/spaces/HuggingFaceTB/SmolVLM-256M-Demo) | |
| Space. | |