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DJ Sri Vigneshwar

Sri-Vigneshwar-DJ
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https://hawky.ai/

AI & ML interests

Co-Founder, CTO @Hawky.ai - Creative Intelligence for Performance Marketing

Recent Activity

reacted to JohnVeds15's post with 🔥 9 days ago
I built a matchup win-rate board for One Piece TCG based on 132 real simulator games. The data is from a single player seat (Sabo v3, OP18 meta) - not a community survey, but real logged game outcomes with confidence intervals. The board shows win rates only when the sample is large enough to be statistically meaningful (n>=5, Wilson lower bound). Below that threshold it shows UNMEASURED rather than a misleading number. Three matchup cells are measured: vs Mihawk 67%, vs Nico Robin 60%, and the Sabo mirror at 50%. The useful part for anyone tracking deck performance: log game results, let the math tell you when a verdict is real vs noise. One season of games is enough to get 3-5 measured matchups. — John (JohnVeds15). I build tools with Claude Code; some of this came out of tracking my own games.https://huggingface.co/spaces/JohnVeds15/optcg-matchup-board
reacted to IsValorum's post with 🔥 9 days ago
Navigating the APEX-I and VAL-APEX Quantization Ecosystem on Hugging Face As the catalog approaches 400,000 total downloads across 69 releases, all models are now structured into dedicated official collections on Hugging Face. Our goal remains consistent: eliminate the compromise between memory overflow and cognitive collapse on consumer hardware (especially 16 GB VRAM GPUs like the RTX 4080, RTX 4070 Ti Super, and RX 7800 XT). Instead of uniform block quantization, all APEX builds use per-tensor mixed precision guided by 4-moment Importance Matrix statistics: - MiniPlus Tier (approx. 14 to 14.8 GiB): Strict 0% 2-bit policy. All MoE routed experts locked at minimum IQ3_XXS with Q4_K/Q6_K attention. Designed for 24 GB setups or 16 GB setups with moderate context. - NanoPlus Tier (approx. 11.5 to 12.3 GiB): Strategic use of IQ2_S/IQ2_XXS exclusively in core feedforward blocks, keeping routers and attention high-precision. Leaves ample VRAM for 16k-32k context on 16 GB GPUs. Explore the active collections: - MoE Standard (MiniPlus & NanoPlus): https://huggingface.co/collections/IsValorum/apex-i-miniplus-v21-standard-moe-6acfee77961c8c0cf5a083b5 - MoE Abliterated (MiniPlus & NanoPlus): https://huggingface.co/collections/IsValorum/apex-i-miniplus-v21-abliterated-moe-6acfee762fc4a8cf2298c55b - 27B Dense Hybrids: https://huggingface.co/collections/IsValorum/val-apex-miniplus-standard-27b-6acfec54ca4ce4cf5cd5bdb6 - Apple Silicon (MLX): https://huggingface.co/collections/IsValorum/mlx-apex-abliterated-6abedb1f9d400e984efe4fc6 Full technical article and sizing breakdown: https://shortbench.vercel.app/
reacted to Gjusev's post with 🔥 9 days ago
MK-SH-V: A 260M Visual Decision Model I've released MK-SH-V, an open-source visual decision model built on H Company's NeoMME-260M. Instead of generating text, it scores candidate answers from screenshots and documents, returning probabilities, a decision or an abstention. Key results: 82.6% GUI accuracy, compared with 73.1% for a 4B zero-shot VLM. +29.4 percentage points in real-image accuracy after incorporating real training data. 86% coverage* with a 95% selective-risk upper bound below 5% on held-out synthetic data. The limitations are documented too. Receipt accuracy remains weak, and calibrated abstention does not yet generalize reliably to real images. The entire experimental process, code and benchmarks are available under Apache 2.0. Model: https://huggingface.co/Gjusev/MK-SH-V GitHub: https://github.com/Gjusev/mk-sh-v Feedback and contributions are welcome!
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