---
language:
- or
license: other
library_name: transformers
tags:
- odia
- text-generation
- kalinga-series
- unfinished
- mystery
- evaluated
---

# 🏛️ KALINGA — Experiment No. 01

*One language. Done properly.*

## The frontier forgot Odia.

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**](https://huggingface.co/datasets/MaelisResearch/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

- [x] Exists
- [x] Remembers its language
- [x] 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.**