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Hariprasad Sundaresan PRO

Hari5115
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PhysiQuanty's profile picture Quazim0t0's profile picture Dcas89's profile picture
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AI & ML interests

LLMs, Fine-tuning, Agentic AI, RAG, Multilingual NLP, Transformers

Recent Activity

reacted to Quazim0t0's post with 🔥 about 18 hours ago
🌼 DaisyChain-Web: train a language model with friends or by yourself with multiple devices, in the browser, no install Open a webpage, share a room link, and every device that joins becomes part of the training cluster. Phones, laptops, old PCs: they connect peer-to-peer over WebRTC and train one shared transformer together, entirely in the browser. What's actually happening under the hood: 🧠 A mini transformer LM trains on FineWeb-Edu, streamed live from the HuggingFace Hub. Each device pulls its own slice (data parallelism), tokenized with our 16.5k-token Spikewhale tokenizer ⚡ Every single multiply runs through verified INT8 neural units, no float fallback. On WebGPU browsers it uses the GPU's DP4A integer dot-product hardware, admitted only after proving bit-identical results against the verified units, with a 3×INT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit) 🔒 Devices average gradients every step under a sync guard: a per-step roster protocol plus weight-hash verification keeps every device's model bit-identical. If anything drifts, training stops instead of silently forking 📊 Live logs show exactly what every device contributes, step by step 💾 When you're done: test generations right on the page, download a checkpoint, or grab the inference kit, a single self-contained HTML file with the weights baked in that runs generations offline, anywhere Works solo too. Every extra device just grows the effective batch. 👉 Try it: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web 🛠 Training framework: https://huggingface.co/DaisyChainAI/DaisyChain-Train Proof of concept: only train with devices you trust. Feedback welcome!
reacted to AbstractPhil's post with 🔥 about 18 hours ago
Massive AlephLM success. The task collective is producing powerful MOE shared knowledge adapters. A serious success and a massive first step towards the next stage. The current family collective results are present here; https://huggingface.co/AbstractPhil/geolip-aleph-qwen/blob/main/exp009_family/results/results.json This is akin to a stackable non-intrusive lora that enables increased shared collective behavior. This includes the three mentioned json tasks, a math task, a tinystories task, and a diffusion task for cifar10. Each adapter anchored to the knowledge within model that already exists while enhancing the knowledge through anchored lookup systems and decision-driven hierarchical access trees. All tasks activate independently upon manual override, all tasks handle direct shared knowledge when left to greedy decoding, each task issued multiple tests alongside to determine fidelity and accuracy throughout the process. The results show the gating is more than willing to hop from sector to sector, using alternating weight shifts from the cooperative anchored systems - even systems never trained for the tasks contributing to the accuracy of the results for other tasks due to the lookup accuracy to the heuristic chains, never having seen the tasks before. Each structure is independently trained and the collective cooperates together through a dense activation network. Full writeup and article https://huggingface.co/blog/AbstractPhil/aleph-autoregression-differentiation-ft2.
reacted to dronefreak's post with 🔥 about 18 hours ago
🚀 Excited to open-source the **UAVid Semantic Segmentation Model Zoo** on Hugging Face. This release includes: * 📦 A **YOLO-compatible mirror** of the UAVid semantic segmentation dataset, preserving the original train/val/test splits while reorganizing the directory structure for plug-and-play use with modern training pipelines. * 🤖 Multiple **YOLO26 semantic segmentation models** trained on UAVid, spanning Nano through Medium variants. * 📊 Detailed model cards with evaluation metrics, per-class IoU, confusion matrices, qualitative results, and training configurations for reproducibility. The goal is to make benchmarking and experimenting with aerial semantic segmentation easier by providing ready-to-use datasets and pretrained models in a consistent format. If you're working on UAV perception, autonomous drones, robotics, remote sensing, or real-time semantic segmentation, I hope these resources are useful. **📦 Dataset:** https://huggingface.co/datasets/dronefreak/UAVid-2020 **🤖 Model Collection:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo Feedback, bug reports, and contributions are always welcome.
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