Update README.md

README.md

@@ -33,21 +33,6 @@ Building efficient, specialist Small Language Models that run on consumer hardwa
 * **Quark-Mod**
   Multi-label content moderation model. 9 categories: toxic, severe_toxic, obscene, threat, insult, identity_hate, cyberbullying, hate_speech, offensive.
 
-## Tokenizers
-
-* **DwarfGoToken**
-  An 8202-token BPE tokenizer built for shell/bash. Uses ByteLevel pre-tokenization with syntax-aware protected tokens for shell operators (2>&1, &&, >>, ||) that would otherwise be split by standard BPE. Built on a 51MB corpus of shell, Python, C, and English text. Two critical bugs were found and fixed during Dwarf-15M training: space loss from incorrect pre-tokenizer configuration, and short shell keywords (fi, do, if) matching as substrings inside English words.
-
-* **GoToken**
-  A BPE tokenizer written in Rust with Python bindings via PyO3. Published on crates.io and PyPI. Provides syntax-aware pre-tokenization for shell/bash patterns. Used as the foundation for DwarfGoToken.
-
-## What We Focus On
-
-* **Specialist over generalist:** A 15M model can't do everything, but it can excel at one thing. Dwarf targets shell/bash; future models will target math/physics.
-* **Honest failure documentation:** When something doesn't work (72M vocabulary problem, zero-shot CoT degradation, tokenizer bugs), we publish the failure and what we learned.
-* **Extreme overtraining for small models:** Following the Phi/SmolLM philosophy — small models need more tokens per parameter, not fewer. Dwarf trains at 2500:1, 125x beyond Chinchilla optimal.
-* **Custom tooling from scratch:** Tokenizers (gotoken, DwarfGoToken), training scripts with multi-source streaming, and inference tools — all built in-house, all open.
-* **Consumer hardware:** Everything runs on an RTX 3070 (8GB) or equivalent. No datacenter required.
 
 ## Links