UDGAM LABS · Language model · 278M parameters, Telugu, base

Vemana-Base

Vemana is a 278M-parameter Telugu language model, trained from scratch on 5.1 billion tokens of public Telugu web text, read about 2.4 times (12.0 billion token positions), with its own 32,000-token Telugu tokenizer. On Telugu Wikipedia pages written after the compared models were released, it predicts text better than every other open base model under 1 billion parameters that we tested, including nanochat-telugu-560M, twice its size (0.361 against 0.383 bits per byte). It is a base model: it continues Telugu text and does not follow instructions. It runs on a laptop-class CPU.

Try it: on your device, in the browser · on Hugging Face Spaces · GGUF for llama.cpp · every checkpoint · code on GitHub · udgamlabs.com/models/vemana · technical report

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM

tok = AutoTokenizer.from_pretrained("udgamlabs/Vemana-Base")
model = AutoModelForCausalLM.from_pretrained("udgamlabs/Vemana-Base")
inputs = tok("తెలుగు భాష", return_tensors="pt")   # the tokenizer adds <s>
out = model.generate(**inputs, max_new_tokens=80, do_sample=True, temperature=0.8, top_p=0.95)
print(tok.decode(out[0], skip_special_tokens=True))

Needs transformers 5.19 or newer (Gemma 4 text layout). Keep prompt and output within 1,024 tokens, the length it was trained at (about 570 Telugu words). On a Mac mini's CPU it writes about 65 tokens per second.

What it writes (sampling as above; English translations are ours):

వర్షాకాలంలో సీజనల్ వ్యాధులు ఎక్కువగా వస్తాయి. అందువల్ల ఆరోగ్యంగా ఉండటం చాలా ముఖ్యం. మీరు వర్షాకాలంలో డెంగ్యూ, మలేరియా, టైఫాయిడ్, వైరల్ ఇన్ఫెక్షన్ల వంటి సీజనల్ వ్యాధుల బారిన పడే ప్రమాదం ఉంది. …

In the rainy season, seasonal illnesses are more common, so staying healthy matters a lot. In the rainy season you are at risk of seasonal illnesses such as dengue, malaria, typhoid and viral infections. …

కంప్యూటర్ అనేది మనకు అత్యంత అవసరమైన వస్తువులలో ఒకటి. కంప్యూటర్లు మనం చేసే చాలా పనులను చేస్తాయి, … ఈ సాధనాలలో ఒకటి CPU. …

A computer is one of the things we need most. Computers do many of the tasks we do, … One of these tools is the CPU. …

Results

Fresh Telugu text. Bits per byte (lower is better) on Telugu Wikipedia pages created after the compared models were released. One scoring script, the same pages and float32 for every model; window 1,024 tokens (Goldfish 512, its limit); measured by us on 27 October 2026.

Model	Parameters	Sep-Oct 2026 (416 pages)	Jul-Aug 2026 (477 pages)	23-26 Oct 2026 (29 pages)
Vemana-Base	278M	0.361	0.385	0.346
nanochat-telugu-560M	561M	0.383	0.408	0.364
Dhee-Pravega-OG Atom	646M	0.393	0.414	0.371
Pothana Base v2	222M (370M with its shared blocks)	0.440 (0.426 on the words its tokenizer covers)	0.456 (0.444)	0.425 (0.410)
Goldfish Telugu 1000mb	125M	0.458	0.480	0.435
Gemma-3-1B	1.0B	0.484	0.502	0.441
Qwen3.5-0.8B-Base	0.87B	0.584
Gemma-3-270m	268M	0.585
Qwen3-0.6B-Base	0.6B	0.692

• Vemana minus nanochat-telugu-560M (paired page bootstrap, 95% interval): −0.022 [−0.024, −0.021] on the Sep-Oct pages, −0.023 [−0.024, −0.022] on Jul-Aug, −0.018 [−0.021, −0.015] on 23-26 Oct. It holds at a 512-token window (−0.017) and without the editor who wrote a third of the Sep-Oct pages (−0.023).
• We fixed the test, the win margin (0.02 bits per byte) and the robustness checks before training, and chose the final checkpoint in advance.

Multiple-choice tests (lm-eval 0.4.13, 0-shot): Belebele-te 25.1%, MILU-te 25.3% (cloze form 30.2%), IndicXNLI-te 35.0%, IndicCOPA-te 58.4%. Chance is 25, 25, 33 and 50% (about 55% on IndicCOPA from answer length alone).

Where it falls short

• Larger models are better. Sarvam-1 (2.5B parameters, about 9× larger) is level with Vemana on the Sep-Oct pages (0.356 against 0.361) and better on the Jul-Aug pages (0.365 against 0.385).
• Knowledge and reasoning tests are at chance, as for most open models under 1B we tested; Qwen3.5-0.8B does better on the letter forms of Belebele (35.6%) and MILU (31.1%). They need a bigger model or more data.
• It makes things up. Asked to continue "తెలుగు భాష" (the Telugu language), it wrote fluently that Telugu Language Day is celebrated on 21 September; it is 29 August.
• It loses the thread. A short story prompt ("ఒక రోజు ఒక రైతు", one day a farmer) turns into the same sentence about a farmer and a cow, repeated with small changes.
• Base model only. It continues text; it does not answer questions or follow requests, and it has no safety tuning.
• 1,024-token context.

How it was trained

• Data: public Telugu web text from three open datasets: Sangraha (AI4Bharat, its human-verified Telugu part), FineWeb-2 (Hugging Face) and HPLT 3.0. Filtered for Telugu script and length, near-duplicates removed across the three, and the Sep-Oct test pages and the multiple-choice test sets removed from the training text. The Jul-Aug and late-October test pages were written in 2026, after the training text was collected (web crawls up to 2024). 5.1B unique tokens.
• Run: 12.0B token positions on one H100 in about 16 hours, about $69 of GPU time, live in public at udgamlabs.com/live/vemana.
• Architecture: Gemma 4 text layout, 18 layers, width 1,024, 8 query and 2 key-value heads, full attention, tied embeddings, our own 32,000-token Telugu tokenizer (byte fallback, so any text can be written).
• Checkpoints: every 10% of the run and the final model, in Vemana-Checkpoints.
• Technical report: the data, tokenizer, training run, the test and the rules fixed before it, and every result, at udgamlabs.com/research/vemana (with a PDF).

Intended use and limitations

A base for research and for building Telugu tools: fine-tuning (chat, summarisation, classification), continued pretraining, and studies of how a small model learns a language. It can produce false, offensive or harmful text and should not be used for decisions about people.

Licence

Apache-2.0. Copyright 2026 Udgam Labs LLP. Training data: Sangraha by AI4Bharat (CC-BY-4.0), FineWeb-2 by Hugging Face (ODC-By 1.0; use also subject to Common Crawl's terms of use), HPLT 3.0 by the HPLT project (CC0 packaging). Details in NOTICE.

Citation

@misc{udgamlabs2026vemana,
  title  = {Vemana: a 278M Telugu language model trained from scratch},
  author = {Prasad, Jitesh K},
  year   = {2026},
  howpublished = {Udgam Labs},
  url    = {https://huggingface.co/udgamlabs/Vemana-Base}
}

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About Udgam Labs. Udgam Labs is an independent AI research lab. We work from first principles: before we build, we understand why things work, whether we are training a model, fine-tuning one or applying AI to a new problem. Udgam (उद्गम) is Sanskrit for origin. udgamlabs.com