ASTERIZER

Building AI from the model to the device. In-house LLMs trained from scratch (100% owned IP), a multilingual tokenizer, a memory layer and on-device inference — no third-party weights.

Models (trained from scratch)

LUNA-100M — 4.5B-token pretrain + RAG/MCP SFT
LUNA-300M — 4.5B-token pretrain (2.7x scale-up)
Ezaris-1B — 27.2B-token pretrain + 32B-token CPT + SFT/Instruct (11x scale-up)

Scaling — LUNA-100M → LUNA-300M → Ezaris-1B (latest base checkpoints, held-out multilingual eval, RTX 4060 Ti, higher is better)

metric	LUNA-100M	LUNA-300M	Ezaris-1B
Parameters	109M	303M	1.21B
Next-token acc (top-1)	32.5%	32.7%	40.5%
Next-token acc (top-5)	56.0%	57.6%	60.3%
Throughput	40.4K tok/s	14.6K tok/s	6.8K tok/s

*LAMBADA-style exact-match is not cross-tokenizer comparable (the 128K tokenizer merges word+punctuation into single tokens), so it is omitted here; next-token columns are the consistent family comparison.

vs top open same-tier models (same held-out corpus, each model with its own tokenizer, RTX 4060 Ti, bf16)

model	train tokens	params	next-tok top-1	bits/byte (lower=better)	LAMBADA	tok/s
Ezaris-1B (ours)	27B + 32B	1.21B	40.5%	0.98	12.9%	6.8K
SmolLM2-1.7B	11T	1.71B	64.2%	0.70	35.1%	8.9K
Qwen2.5-1.5B	~18T	1.54B	58.7%	0.69	25.7%	6.1K
TinyLlama-1.1B	3T	1.10B	59.3%	0.78	35.1%	4.7K

Honest notes:

Ezaris-1B is a 27.2B pretrain + 32B CPT base (~1/100th of the reference models' training tokens). The quality gap (bits/byte 0.98 vs 0.69–0.78) is real and is the focus of our ongoing CPT/SFT program.
Per-position next-token favors fine tokenizers (TinyLlama's 32K = many easy positions; Ezaris's 128K = fewer, harder positions with 4x the vocab classes). bits/byte is the tokenizer-independent metric.
Where Ezaris wins by design: ~2x fewer tokens per text, ~2x faster end-to-end per text, native Indic coverage, and code next-token accuracy 66% (beats TinyLlama's 60%).

Tokenizer — multilingual BPE (tokens per 1,000 bytes; lower = better)

tokenizer	Indic avg*	Hindi	English	Code	ALL
Ezaris 128K	97	127	219	276	178
OpenAI o200k (GPT-4o)	137	122	200	222	176
Sarvam-1	110	101	241	323	193
Ezaris 64K	111	147	235	305	193
Ezaris 32K	130	174	260	338	217
GPT-NeoX 50K (baseline)	574	386	202	268	319
Llama-3 128K	592	199	202	218	301
TinyLlama 32K	756	416	235	294	358
SmolLM2 50K	874	418	211	262	387

*Indic avg = Kannada/Telugu/Tamil mean.

Same text, tokens needed vs Ezaris-128K (lower = better): GPT-NeoX ~6x on Indic, 1.8x overall · Llama-3 ~6x on Indic, 1.7x overall · SmolLM2 2.2x overall · TinyLlama 2.0x overall · Sarvam-1 1.14x on Indic · OpenAI o200k 0.99x overall (on par with GPT-4o's tokenizer).

Honest trade-offs: Ezaris deliberately spends its 128K vocab budget on South-Indian scripts (that is the product bet) — English ~9% and code ~24% denser under o200k, and Hindi lags Sarvam-1 ~25% (next optimization target).

Programs

LUNA-100M · LUNA-300M · Ezaris
Tokenizers: LUNA (100M/300M) · Ezaris (128K/64K/32K)