jaithree

AI & ML interests

[gemma]: Does it feel like you're building a personal "operating system" for thought? [me]: no, you are just a smart ass grep for thoughts

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reacted to Javedalam's post with 🔥 about 8 hours ago

A 3B Vision Model Running at 13 Tokens/Second — on a Phone AI is moving from the cloud into your pocket. I just tested Liquid AI's newly released LFM2.5-VL-3B locally on a Samsung Galaxy S26. The Q8_0 model ran through llama.cpp with Vulkan acceleration on the Qualcomm Adreno 840 GPU. I uploaded a real webpage screenshot from another device and asked the model to describe it. It worked—and generated at 13.11 tokens per second. That's significant for a 3B-class vision-language model running entirely on a phone. In my testing, LFM2.5-VL-3B is the most capable local vision model I've run on the S26 so far. Liquid AI is building specifically for this world. Its focus is edge AI: capable models designed to run locally on phones, laptops, vehicles and other resource-constrained hardware instead of depending entirely on cloud inference. Its LFM family targets low memory usage, fast inference and deployment across CPUs, GPUs and NPUs. LFM2.5-VL-3B brings vision into that strategy. It is an open-weight multimodal model capable of image understanding, OCR, document extraction, visual question answering and spatial reasoning. Liquid provides the weights publicly, including an official GGUF release for llama.cpp. This is where open-weight AI gets interesting. A phone can now hold the model, process the image and generate useful multimodal responses locally at interactive speed. No cloud GPU. No remote inference API. The phone is the AI computer.[LFM2.5-VL-3B — Hugging Face](https://huggingface.co/LiquidAI/LFM2.5-VL-3B?utm_source=chatgpt.com) [LFM2.5-VL-3B-GGUF — Hugging Face](https://huggingface.co/LiquidAI/LFM2.5-VL-3B-GGUF?utm_source=chatgpt.com)

reacted to SeaWolf-AI's post with 👍 about 9 hours ago

🧬 Your AI can design a malaria drug candidate. Can it tell you whether it's any good? Open Discovery Challenge #1 — Malaria is live. Design a molecule with any model — OpenAI, Claude, Gemini, Qwen, KIMI, DeepSeek, open weights, or by hand — submit it as SMILES, and it's scored in minutes on whole-cell activity, target binding, selectivity over the human enzyme, ADMET, novelty and synthesisability. You can check the scoring instead of trusting it. Approved drugs sit on the same leaderboard as the entries: DSM265, a clinical-stage antimalarial, scores 50.9. Teriflunomide — approved, but it hits the human enzyme — scores 2.8. Caffeine scores 1.8. If the clinical candidate lands on top and coffee lands at the bottom, the scorer discriminates. We caught 14 defects before opening — conventional toxicity cutoffs rejected all three approved antimalarials and coffee. All written up, along with the rule we now hold everything to: a gate that rejects an approved drug is a broken gate. Your molecule stays yours. No patent interest, nothing into our pipeline. You choose whether it's published — and publishing can cost you patentability, so we say so. USD 1,000 to the top entry when Season #1 closes 30 September 2026 — not payment for your tokens, but a way of saying the work had worth. Malaria killed ~597,000 people in 2023, three quarters of them children under five. Not for want of chemistry — for want of a market. No chemistry needed: the guide ships five prompts you can paste straight into your model, and the full rubric is published. 📖 https://huggingface.co/blog/FINAL-Bench/open-discovery-challenge 🚀 https://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge Computational assessments of candidates — not measurements, not claims of efficacy.

liked a model about 11 hours ago

ReadyArt/Omega-Convergence-27B-v1.0

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