Point org-card tokenizer link to merged LUNA-Tokenizer-1B repo

README.md

@@ -1,30 +1,30 @@
----
-title: ASTERIZER
-emoji: 🚀
-colorFrom: gray
-colorTo: gray
-sdk: static
-pinned: false
----
-
-# ASTERIZER
-
-**Building AI from the model to the device** — self-hosted language models, a memory layer, application framework, and on-device AI, engineered in-house with 100% owned IP.
-
-**Models (trained from scratch)**
-- [LUNA-100M](https://huggingface.co/ASTERIZER/LUNA-100M) — 4.5B-token pretrain + RAG/MCP SFT
-- [LUNA-300M](https://huggingface.co/ASTERIZER/LUNA-300M) — 4.5B-token pretrain (2.7× scale-up)
-- LUNA-1B — in progress
-
-**Scaling benchmarks — LUNA-100M → LUNA-300M** *(latest base checkpoints, same 4.5B-token corpus, held-out ~100K-token eval on RTX 4060 Ti — higher is better)*
-
-| metric | LUNA-100M | LUNA-300M | gain |
-|---|---|---|---|
-| Next-token prediction accuracy | 32.5% | 32.7% | ↑0.8% |
-| Word-prediction accuracy (LAMBADA-style) | 16.3% | 17.1% | ↑4.4% |
-| Throughput (RTX 4060 Ti) | 39.2K tok/s | 14.5K tok/s | 2.7× params |
-
-**Tokenizer (multilingual BPE)**
-- [LUNA-1B-Tokenizer](https://huggingface.co/ASTERIZER/LUNA-1B-Tokenizer) — 128K / 64K / 8K vocab
-- **2.8× better compression** than GPT-NeoX, **67% fewer tokens** on Indic text (Kannada 7×, Telugu 6×, Tamil 5×)
-- Beats `cl100k_base` on Indic; competitive with Sarvam (34/55 head-to-head wins)
+---
+title: ASTERIZER
+emoji: 🚀
+colorFrom: gray
+colorTo: gray
+sdk: static
+pinned: false
+---
+
+# ASTERIZER
+
+**Building AI from the model to the device** — self-hosted language models, a memory layer, application framework, and on-device AI, engineered in-house with 100% owned IP.
+
+**Models (trained from scratch)**
+- [LUNA-100M](https://huggingface.co/ASTERIZER/LUNA-100M) — 4.5B-token pretrain + RAG/MCP SFT
+- [LUNA-300M](https://huggingface.co/ASTERIZER/LUNA-300M) — 4.5B-token pretrain (2.7× scale-up)
+- LUNA-1B — in progress
+
+**Scaling benchmarks — LUNA-100M → LUNA-300M** *(latest base checkpoints, same 4.5B-token corpus, held-out ~100K-token eval on RTX 4060 Ti — higher is better)*
+
+| metric | LUNA-100M | LUNA-300M | gain |
+|---|---|---|---|
+| Next-token prediction accuracy | 32.5% | 32.7% | ↑0.8% |
+| Word-prediction accuracy (LAMBADA-style) | 16.3% | 17.1% | ↑4.4% |
+| Throughput (RTX 4060 Ti) | 39.2K tok/s | 14.5K tok/s | 2.7× params |
+
+**Tokenizer (multilingual BPE)**
+- [LUNA-1B-Tokenizer](https://huggingface.co/ASTERIZER/LUNA-Tokenizer-1B) — 128K / 64K / 8K vocab
+- **2.8× better compression** than GPT-NeoX, **67% fewer tokens** on Indic text (Kannada 7×, Telugu 6×, Tamil 5×)
+- Beats `cl100k_base` on Indic; competitive with Sarvam (34/55 head-to-head wins)