Update README.md

README.md

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 * **Quark-135M**
   Bilingual (Italian + English) general-purpose model. 135M parameters, 30 layers, 9 attention heads (3 KV, GQA), SwiGLU, RMSNorm, RoPE θ=10k. Trained on 15B+ tokens. Published benchmarks: HellaSwag 31.37%, ARC-Easy 41.46%, PIQA 61.26%.
 
-* **Quark-72M** *(archived — research artifact)*
-  A 71.7M parameter model that taught us an expensive lesson. With vocab_size=65536 and d_model=512, the embedding matrix consumed ~33.5M of the 71.7M total parameters — nearly half the budget in pure lookup table. Effective transformer capacity was ~35M parameters, explaining why it underperformed the nominally smaller Quark-135M on every benchmark (PIQA 54.57% vs 58.32%, ARC-Easy 32.10% vs 47.73%). Additionally, zero-shot chain-of-thought prompting actively degraded performance, dropping ARC-Easy from 33% to 25.5% (random guess level). This model remains published with its limitations honestly documented. Every architectural decision in Dwarf-15M — the compact 8K vocabulary, the syntax-aware tokenizer, the instruction data mixed into pretraining — was a direct response to what went wrong here.
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-* **Quark-Mod**
-  Multi-label content moderation model. 9 categories: toxic, severe_toxic, obscene, threat, insult, identity_hate, cyberbullying, hate_speech, offensive.
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 ## Links
 
 * Models and tokenizers: [HuggingFace](https://huggingface.co/ThingAI)