40+ million speakers. A literary tradition older than most languages the frontier has ever shipped. And the biggest labs in the world trained on 200 languages — and left this one out, as if it had never existed.

Kalinga exists because we refuse to accept that.

This is Experiment No. 01. The first of a series that will not stop.

What we found, and what we kept

We started with 28,549,762 rows of raw Odia.

More than half of it was the same text wearing different clothes. We removed it all. What remains is signal — and nothing else:


Rows kept	12,438,948 — nothing repeated, nothing padded
Total text	~5.9 billion characters of Odia
English-aligned	48.7 % — real alignments, not flags
Per-row	median 203 · mean 474 characters
Quality gate	≥ 20 chars · ≥ 10 % Odia script · 63 shards · 3.7 GB
Composition	monolingual 68.9 % · pretrain 15.4 % · parallel 7.7 % · instruction 5.3 % · QA 1.5 % · web · encyclopedic · speech · sentiment

The frontier's approach: 200 languages, two minutes each.

Kalinga's approach: one language, 5.9 billion characters, nothing repeated.

The corpus has never been published anywhere. While licensing of legacy sources is resolved, it stays private — this model is the closest thing to it that exists in public.

The receipts

The first eval numbers exist. They are not flattering. That is the point.

Evaluated on Odia Eval Benchmark (60 rows per task × 7 tasks = 420 rows, greedy decoding, max 96 new tokens, 2× T4):

Task	Kalinga-01	sarvam-1 (base)
multiple_choice (acc)	0.0 %	13.3 %
QA span F1	0.0	0.03
math (acc)	0.0 %	0.0 %
classification (acc)	0.0 %	0.0 %
NER token F1	0.0	0.14
translation chrF	0.096	0.04
generation	logged	logged

What the numbers say: Kalinga-01 emits <reserved_token> ids in every one of the 420 generations — a sign the checkpoint's output layer and embeddings are misaligned with its tokenizer, i.e. the experiment is unfinished (as tagged), not merely undertrained. The base model it was built on, sarvam-1, at least produces coherent Odia. Kalinga does not — yet.

These numbers will get better. When they do, they get written here. Until then, this card stays honest.

Status

Exists
Remembers its language
Evaluated — numbers above. honest, not flattering.
Explained
Named — you'll recognize it when you see it.
Freed — the corpus, when licensing allows.

Watch. The silence will not last.

For the 40 million

Your language was never the problem. The attention was.

It's free. Take it. Use it. That's the point.

If you must:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SauravMahalik/kalinga-01"

model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

text = "..."  # a sentence, in a language it knows
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

— Experiment No. 01 · Kalinga Series

The series has already begun.