A 450M Vision Model Fine-Tuned on 50,000 Browser Screenshots Scored 44/100, Beating Gemma 4 E4B and Qwen 3.5 2B but Not Qwen 3.5 4B

r/LocalLLaMA (80 upvotes), full benchmark write-up at webbrain.one/blog/tiny-vision-models-compared-ii·medium signalXBlueskyLinkedInCopy link

Starting from LFM2.5-VL-450M, which scored 0/100 strict passes on a held-out 100-case browser screenshot benchmark, the author fine-tuned on 16,646 screenshots to reach 30/100, expanded to 50,000 examples weighted toward multilingual OCR, forms, auth screens, modals, tables and loading states to reach 36/100, then exported to a Q4 decoder plus FP16 vision ONNX config for WebGPU which scored 44/100. Comparison scores on the same set: Qwen 3.5 2B at 39, Gemma 4 E4B at 41, Qwen 3.5 4B at 55. Training was supervised LoRA at rank 16, about 4.1M trainable parameters, one epoch on a single RTX 4090, with labels generated by Qwen 3.6 35B, and the author credits matching the output contract to what the browser agent actually consumes rather than generic captions.