Compressed a RoBERTa AI-text detector from ~500 MB to ~97 MB so it runs client-side in the browser, plus a 26 MB image detector [P]
I wanted an AI text and image detector that doesn't need you to upload anything, so I took two existing open detectors and got them running fully client-side with ONNX Runtime Web.
Proof1 community thread
Key points
- The base models are fakespot-ai/roberta-base-ai-text-detection-v1 (Apache-2.0) and Community Forensics (commfor-model-384, MIT).
- Text model (about 500 MB in fp32, 97 MB after compression)
- I fine-tuned it lightly on a mix of human and AI text (HC3, GPT-wiki-intro, HAP-E, WildChat, Gutenberg and some text I generated locally with Gemma-3-1B).
- This is a quantized Community Forensics ViT with calibrated output scores.
Sources (1)
- [1]Compressed a RoBERTa AI-text detector from ~500 MB to ~97 MB so it runs client-side in the browser, plus a 26 MB image detector [P]r/MachineLearning (top, daily) · Oct 12, 07:44 AM
I wanted an AI text and image detector that doesn't need you to upload anything, so I took two existing open detectors and got them running fully client-side with ONNX Runtime Web.
The base models are fakespot-ai/roberta-base-ai-text-detection-v1 (Apache-2.0) and Community Forensics (commfor-model-384, MIT).
Extractive summary: sentences quoted from the sources.
Before this
- Oct 12, 2026QLoRA on ExLlamaV3 Quants - Train @ 2-8bpw
- Oct 12, 2026Open-sourced my image-dataset prep tool — quality scoring, dedupe, leak-safe splits, Kohya export (pure-Python, MIT)
- Oct 11, 2026Gemma4 E4B EXL3 - Alternative to Llama.cpp on Jetson Orin
- Oct 10, 2026Engineer / developer observations of Gemma4-31B, Qwen3.8-27B, and 6.1-Sol for software engineering work
- Oct 9, 2026Microsoft's Decision-1 model enters the fast-growing AI decision model race
- Oct 9, 2026Qwen/Qwen-Image-2.1-Turbo
