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dipankarsarkar
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ยท
https://www.dipankar.cc

AI & ML interests

Building the AI-native stack. Agents as infrastructure, safety as architecture, performance as plumbing. I publish the receipts: papers, datasets, demos.

Recent Activity

reacted to raincandy-u's post with ๐Ÿ”ฅ about 1 hour ago
20K parameters can tell a story. ๐Ÿš€ ๐Ÿค— We trained a ~20k-parameter Transformer that can actually write stories! https://huggingface.co/raincandy-u/MacroStories โ†’ ~50ร— smaller than the 1M-parameter TinyStories model โ†’ ~3,000ร— smaller than AlexNet โ†’ 81 KB in FP32 yayyy the whole model. เซฎ หถแต” แต• แต”หถ แƒ She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block โ€” recurrently applied 4 times with shared weights. Despite having only 19,969 parameters, she can maintain a simple narrative across 100โ€“300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome. She runs extremely fast on CPU โ€” no GPU required. The entire model is tiny enough to load almost instantly! โ˜บ๏ธ
reacted to joeyouss's post with ๐Ÿ”ฅ about 2 hours ago
Introducing Precision-3! Note: We'll walk through the model and take questions in our webinar this Thursday, October 8 at 5 PM CEST : https://app.livestorm.co/pyannote-ai/precision-3 Different tasks want different diarization errors: TTS data prep needs overlap removed, transcription needs every quiet word kept. Precision-3 lets you shift the operating point at inference time, without retraining, via vad_sensitivity and crosstalk_sensitivity (log-prior shifts, not post-hoc thresholds). Results across the 11 DIHARD domains: - VAD F1: 95.8% (vs 95.0% community-1, 91.9% Silero VAD) - Overlap detection F1: 59.7% vs 48.2% - The optimal VAD setting varies by domain, from โˆ’0.8 (Restaurant) to +2.0 (Audiobooks) - Averaged over 15 datasets, DER drops from 16.02 (Precision-2) to 14.35, mostly from lower speaker confusion. Write-up: https://pyannote.ai/blog/tune-vad-crosstalk-speaker-diarization Let us know what you're building with pyannote!
liked a dataset about 3 hours ago
harvardMadsys/freeinference_agentic_trace
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