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reacted to Shrijanagain's post with 🔥 about 6 hours ago
​We are thrilled to announce the launch of SKT-OMNI-CORPUS-146T-V1, a massive-scale, high-quality dataset designed to power the next generation of Foundation Models (LLMs) from scratch. ​Developed at SKT AI LABS, this corpus is not just a collection of data; it’s a mission to decentralize high-grade AI training for regional languages and global knowledge. ​💎 Key Highlights: ​•• Massive Scale: Targeting a multi-terabyte architecture for 146T-level tokenization. •• ​Pure Quality: Curated from 500+ Elite Sources •• ​Structured for MoE: Perfectly sharded into 3.5GB standardized units (SKT-𝕻 series) for seamless distributed training. ​🤝 Open for Collaboration! ​We are looking for AI researchers, CUDA engineers, and data scientists to join us in this journey of building Project Surya and the ST-X Series models. Whether it's optimization, custom tokenization, or architecture design—let’s build the future together. ​Explore the Dataset on Hugging Face: 🔗 https://huggingface.co/datasets/Shrijanagain/SKT-OMNI-CORPUS-146T-V1 DSR -- 🔗 https://huggingface.co/datasets/Shrijanagain/SKT-DSRx10000 ​#AI #MachineLearning #OpenSource #IndicAI #SKTAILABS #LLM #BigData #HuggingFace #InnovationIndia
reacted to Oldmangrizzz's post with 👀 about 6 hours ago
I would like to share with you guys something that I’ve spent the last three years of my life working on and it’s absolutely nothing else at all. I would absolutely enjoy and welcome. Any discussion that anybody feels like having regarding the subject matter… -Griz https://huggingface.co/Grizzlymedicine/HUGH
reacted to Entz's post with 👀 about 6 hours ago
London Property Market Analyst — GPT + HM Land Registry + Gradio (full pipeline breakdown) --- Built a production RAG-style chatbot over 32 million rows of official UK property transaction data. Sharing the architecture in case it's useful for others building structured-data Q&A systems. This is the latest step in a project that started in 2021 as a basic Python analyst script, grew into automatic PDF report generation, then became a fully automatic daily pipeline — and now a conversational AI anyone can query. **Live demo:** https://uk-property-app.entzai.com/ **My Space:** https://huggingface.co/spaces/Entz/uk-property-app --- ### The problem with LLMs over tabular data The naive approach — dump your CSV into the context window — breaks down fast at scale. The raw HM Land Registry file is a 4–5GB CSV covering 32 million transactions across England & Wales. I filtered it to ~1.76M London transactions (2010–2026), but even that doesn't fit in any context window, and even if it did, asking GPT to do GROUP BY in its head is asking for hallucinations. The solution: **a structured analytics layer between the LLM and the data.** --- ### Architecture ``` User query (natural language) ↓ Triage Agent (GPT) — classifies intentions. — extracts structured params: district, property type, new/old build, time window, metric (median/mean/count) ↓ Analytics Engine (pure Python + Pandas) — queries pre-aggregated Parquet files (not raw CSV) — returns structured JSON ↓ Synthesis Agent (GPT) — receives structured JSON, writes prose analysis — hard rules prevent hallucination of years, ranges, stats ↓ Chart Agent (Matplotlib) — 10 chart types: line, multi-line, stacked bar, h-bar, diverging bar, band trend, growth ranking, table — returned as base64 PNG → gr.Image on frontend ```
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