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Independent builder training small/efficient models from scratch on consumer hardware.

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GLM-5.3
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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
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