Update README.md

README.md

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 ---
-title: README
-emoji: 📚
-colorFrom: red
-colorTo: purple
-pinned: true
+title: ThingsAI
+type: org
+tags:
+- slm
+- llm
+- pytorch
+- amdl
+- math
+- code
 ---
 
-# Welcome to ThingsAI! 🤗
 
-**Building efficient, bilingual AI models that run anywhere.** 🇮🇹 🇬🇧
+# Welcome to ThingsAI. Building highly efficient, logic-driven Small Language Models that run anywhere.
 
----
-
-## 🤖 Our Models
-
-### [Quark-135M](https://huggingface.co/ThingAI/Quark-135m-Bilingual)
-A lightweight bilingual (Italian + English) language model with **135M parameters**. Features GQA, SwiGLU, RMSNorm, and RoPE. Trained on 50B+ tokens of curated Italian and English data.
+## Our Models
 
-### Quark-270M *(training now)*
-Our most powerful small model — **270M parameters** with 32 layers, 768 hidden dimensions, and 65K vocabulary. Currently in active training on 10B+ tokens with planned scaling to 135B tokens.
-
-### [Quark-Mod](https://huggingface.co/ThingAI/Quark-Mod)
-A multi-label moderation model covering **9 categories** for safe AI deployment: toxic, severe_toxic, obscene, threat, insult, identity_hate, cyberbullying, hate_speech, offensive.
-
----
+* **Quark-135M**
+  A lightweight bilingual (Italian + English) language model with 135M parameters. Features GQA, SwiGLU, RMSNorm, and RoPE. Trained on 50B+ tokens of curated data.
+* **Quark-270M**
+  Our scaled small model featuring 270M parameters, 32 layers, 768 hidden dimensions, and a 65K vocabulary. Designed for extended bilingual capabilities.
+* **Quark-Math-Code (~36M)**
+  Our ultra-compact, deep-thin architecture (~36M parameters, 14 layers, 65K vocabulary) engineered specifically for STEM, coding, and mathematical reasoning. Actively pre-training on a 5B token target with a hardened Chain-of-Thought (CoT), OpenWebMath, and pure-code mix.
+* **Quark-Mod**
+  A multi-label moderation model covering 9 categories for safe AI deployment: toxic, severe_toxic, obscene, threat, insult, identity_hate, cyberbullying, hate_speech, offensive.
 
-## 🎯 What We Focus On
+## What We Focus On
 
-- ⚡ **Small, efficient architectures** — GQA, weight tying, deep-thin design
-- 🌍 **Bilingual training** — Italian + English from scratch
-- 🔓 **Open-source everything** — weights, code, datasets
-- 💻 **Real-world deployment** — runs on consumer hardware
+* **Hyper-Efficient Architectures:** Mastering the sub-1B parameter space using GQA, Grouped-Query Attention, and deep-thin layer scaling.
+* **Embedded Chain-of-Thought (CoT):** Hardcoding step-by-step reasoning tokens into the pre-training phase of tiny models to punch far above their weight class in logic benchmarks.
+* **Bilingual & Specialty Data:** Multi-source streaming pipelines fusing Italian, English, high-density mathematics, and code.
+* **Open-Source & Real-World Deployable:** Everything from weights to datasets is open. Tailored to achieve massive throughput on consumer GPUs and edge hardware.
 
----
-
-## 📂 Resources
-
-| | |
-|---|---|
-| 📚 [Quark-135M-Bilingual](https://huggingface.co/ThingAI/Quark-135m-Bilingual) | Our flagship bilingual model |
-| 🛡️ [Quark-Mod](https://huggingface.co/ThingsAI/Quark-Mod) | Multi-label content moderation |
-| 📝 [HuggingFace Community](https://huggingface.co/ThingsAI) | All our models and datasets |
-| 💻 [GitHub](https://github.com/overcastlab) | Code, scripts, and tools |
-
----
+## Resources
 
-<p align="center">
-  <sub>Made with ❤️ by ThingsAI · <a href="https://things-ai.org">Website</a> · <a href="https://github.com/overcastlab">GitHub</a></sub>
-</p>
+* **Quark-135M-Bilingual:** Our flagship general-purpose bilingual model.
+* **Quark-Mod:** Multi-label content moderation for production pipelines.
+* **HuggingFace Community:** All our released models, tokenizers, and custom datasets.
+* **GitHub Open Source:** Training scripts, custom multi-source streaming iterators, and deployment tools.