jiteshkprasad
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8 following Building udgamlabs.com
replied to their post about 13 hours ago We just released Vemana, a 278M Telugu base model trained from scratch, live in public, in about 16 hours on one H100. It learned from 5.1B tokens of public Telugu web text (Sangraha, FineWeb-2 and HPLT 3.0), seen about 2.4 times, so 12B tokens of training, with its own Telugu tokenizer.
On 416 Telugu Wikipedia pages written in Sep-Oct 2026, after nearly all the compared models were released, it scores 0.361 bits per byte. That is the lowest of every open base model under 1B we tested: nanochat-telugu-560M 0.383, Dhee-Pravega-OG Atom 0.393, Pothana Base v2 0.426, Gemma 3 1B 0.484. Same script and pages for every model, and the win rule was fixed before the run.
Sarvam-1 (2.5B) is still level or better, and on knowledge tests like Belebele and MILU Vemana is at chance, like most other small models. I think it’s most useful as a base for Telugu fine-tunes and for studying how a small model learns a language, so we put out all 12 checkpoints also. And you can run it right in your browser, on your own device.
Model: https://huggingface.co/udgamlabs/Vemana-Base
Checkpoints: https://huggingface.co/udgamlabs/Vemana-Checkpoints
GGUF: https://huggingface.co/udgamlabs/Vemana-Base-GGUF
Try it in your browser: https://udgamlabs.com/models/vemana
Technical report: https://udgamlabs.com/research/vemana replied to their post about 13 hours ago Okay so, we at https://huggingface.co/udgamlabs just released Lekhani, a Hindi transliterator that is 0.87 MB in total. You type Hindi in English letters, like "kaise ho", and it writes कैसे हो.
The whole thing is a 0.81M-parameter model at 4 bits, a 200,000-word list and a 5 KB JavaScript runner with no dependencies. It runs in the browser or in Node, so nothing you type leaves your device.
On the Aksharantar Hindi test it gets 65.9% of words exactly right, 7 points more than IndicXlit's model on its own (58.9%), which has 14x more parameters. IndicXlit's full package is still ahead at 68.7%, but its word list alone takes 358 MB of memory, about 400 times the size of all of Lekhani. I think for a web page or an app, that difference matters a lot.
It reads one word at a time, so "main" and "mein" can come out the same, and a few common words still come out wrong. The card has the full table, the other open models we measured and where it falls short.
It's Apache-2.0 for the model and runner, and CC0 for the word list. If you type Hindi in English letters, try it and tell me which words it gets wrong.
Model: https://huggingface.co/udgamlabs/Lekhani
Try it: https://udgamlabs.com/models/lekhani
Code: https://github.com/udgamlabs/lekhani replied to their post about 13 hours ago Okay so, we at https://huggingface.co/udgamlabs just released Lekhani, a Hindi transliterator that is 0.87 MB in total. You type Hindi in English letters, like "kaise ho", and it writes कैसे हो.
The whole thing is a 0.81M-parameter model at 4 bits, a 200,000-word list and a 5 KB JavaScript runner with no dependencies. It runs in the browser or in Node, so nothing you type leaves your device.
On the Aksharantar Hindi test it gets 65.9% of words exactly right, 7 points more than IndicXlit's model on its own (58.9%), which has 14x more parameters. IndicXlit's full package is still ahead at 68.7%, but its word list alone takes 358 MB of memory, about 400 times the size of all of Lekhani. I think for a web page or an app, that difference matters a lot.
It reads one word at a time, so "main" and "mein" can come out the same, and a few common words still come out wrong. The card has the full table, the other open models we measured and where it falls short.
It's Apache-2.0 for the model and runner, and CC0 for the word list. If you type Hindi in English letters, try it and tell me which words it gets wrong.
Model: https://huggingface.co/udgamlabs/Lekhani
Try it: https://udgamlabs.com/models/lekhani
Code: https://github.com/udgamlabs/lekhani View all activity view post Okay so, we at udgamlabs just released Lekhani, a Hindi transliterator that is 0.87 MB in total. You type Hindi in English letters, like "kaise ho", and it writes कैसे हो. The whole thing is a 0.81M-parameter model at 4 bits, a 200,000-word list and a 5 KB JavaScript runner with no dependencies. It runs in the browser or in Node, so nothing you type leaves your device. On the Aksharantar Hindi test it gets 65.9% of words exactly right, 7 points more than IndicXlit's model on its own (58.9%), which has 14x more parameters. IndicXlit's full package is still ahead at 68.7%, but its word list alone takes 358 MB of memory, about 400 times the size of all of Lekhani. I think for a web page or an app, that difference matters a lot. It reads one word at a time, so "main" and "mein" can come out the same, and a few common words still come out wrong. The card has the full table, the other open models we measured and where it falls short. It's Apache-2.0 for the model and runner, and CC0 for the word list. If you type Hindi in English letters, try it and tell me which words it gets wrong. Model: udgamlabs/Lekhani Try it: https://udgamlabs.com/models/lekhani Code: https://github.com/udgamlabs/lekhani See translation
Laghu: a 152M chat model trained from scratch for $146. Everything is open.
udgamlabs just released Lekhani, a Hindi transliterator that is 0.87 MB in total. You type Hindi in English letters, like "kaise ho", and it writes कैसे हो.