@apolloaiapp for Android now has Live Updates 🚀 You can track the progress and see the result as soon as it’s ready — without keeping the app open And the best part? You can try it with the latest @liquidai models

This is huge albeit expected improvement from @OpenAI for GPT-6 Astra model. Great work, best LLM out there! What is eye-catching - it outperformed two strong specialists models! Notable that our 2.6B specialist is still the best on single-step retrosynthesis 🏆#insilicoSOTAFM#

training models from scratch gives you so much control on core competencies of the final models. Say you wanna deploy a fast model for cybersecurity applications, you can verifiably steer the model during “pre”-training towards fundamental behavior that during “post”-training” allows for the strongest guardrails reliability & resiliency. This you cannot do with a checkpoint you don’t know its pre-training data, and initial conditions.

🧪 NEW SOTA in chemical synthesis Building molecules is hard. There are countless possible routes, but only some actually work. We trained a 2.6B LFM model by @liquidAI in #MMAIGym# to find them. 📈 +7% vs. the best specialist tool 🚀 +27% vs. frontier LLMs 💥 2.6B punches way above its weight #insilicoSOTAFM#

Guys and Girl nerds, the tiny 500m-1.2B models trained for a single task are taking off cause they are dirt cheap to run and do that task better then frontier models. Us Local AI folks can run AND train these at home. (I will soon) Actually ☝️🤓 For a training a highly specialized 1B model, 1,000 to 5,000 high-quality, varied examples are often enough to achieve frontier-level performance on that specific task. Not 50,000 to 1 million like with big models. Checkout this blog article by @ liquidai where they talk about their tiny task models. "LFM2-Extract – A 350M and 1.2B multilingual models for data extraction from unstructured text, like turning invoice emails into JSON objects. LFM2‑350M‑ENJP‑MT –  A 350M model for bidirectional English ↔ Japanese translation. LFM2‑1.2B‑RAG – A 1.2B model optimized for long‑context question answering in RAG pipelines. LFM2‑1.2B‑Tool – A 1.2B model built for function calling and agentic tool use. LFM2‑350M‑Math –  A 350M reasoning model for solving mathematical problems. Luth-LFM2 – An additional community-driven series of French fine-tunes to enable general-purpose assistants for on-device chat." Read more here:

It was a pleasure to work with #LFM# by @liquidai @ramin_m_h to train our new #insilicoSOTAFM# model for single-step retrosynthesis 🧪on ~46M reactions! It provides plenty of diverse reactions that only partially intersect with the reaction space by previous SOTA small models! 🔎👀