acd23/cad-3d-reconstruction
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kingjones777/OxCoder-9B-MTP-ROCmFP4-GGUF reacted to jiteshkprasad's post with 🔥 about 2 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/vemanaOrganizations
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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: udgamlabs/Vemana-Base
Checkpoints: udgamlabs/Vemana-Checkpoints
GGUF: udgamlabs/Vemana-Base-GGUF
Try it in your browser: https://udgamlabs.com/models/vemana
Technical report: https://udgamlabs.com/research/vemana
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: udgamlabs/Vemana-Base
Checkpoints: udgamlabs/Vemana-Checkpoints
GGUF: 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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S²-MAS update: YN decision models, a 500M-persona resource, and household consumption prediction.
YN-mini, YN-Lite-4B and YN-master-9B map household states and complete choice menus to option probabilities. The paper now uses these names throughout.
Completed population application: 12M distinct households, 24M one-shot choices and 288M monthly ledger states in 3h59m17s on 14 GPUs. YN-mini-MLX achieves 74.55% accuracy and 38.37% question-macro F1 on the 240,458-record consumer-choice test.
New 1,060-household results bring standalone 4B/9B forecasts together with fully rational and advised policies. Standalone 9B's annual MAE is 3.42% below the deterministic rational benchmark and 2.11% below carry-forward; its population mean remains 19.7% below observed consumption. The paper reports income/wealth group MAE and explains the consumption–cash paths behind these differences. The household 4B uses the head-only 200k variant.
Relabeling the policy year from 2018 to 2031 leaves selected answers unchanged in 12 paired tests per model; separate outcome-note interventions reveal task-dependent information sensitivity. Paired policy results subtract separately simulated baseline paths from tax-cut paths.
The 56-page paper presents learned decision policies as another route to large-scale heterogeneous-household modeling.
Article: https://huggingface.co/blog/ya9919/s2-mas
Updated PDF: https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf
Paper + source release: https://github.com/KataDavidXD/s2-mas/releases/tag/v67
Models: https://huggingface.co/collections/ya9919/s-mas-models-sharp
Updated 28 October 2026. Research contact: yl9919@connect.hku.hk.
YN-mini, YN-Lite-4B and YN-master-9B map household states and complete choice menus to option probabilities. The paper now uses these names throughout.
Completed population application: 12M distinct households, 24M one-shot choices and 288M monthly ledger states in 3h59m17s on 14 GPUs. YN-mini-MLX achieves 74.55% accuracy and 38.37% question-macro F1 on the 240,458-record consumer-choice test.
New 1,060-household results bring standalone 4B/9B forecasts together with fully rational and advised policies. Standalone 9B's annual MAE is 3.42% below the deterministic rational benchmark and 2.11% below carry-forward; its population mean remains 19.7% below observed consumption. The paper reports income/wealth group MAE and explains the consumption–cash paths behind these differences. The household 4B uses the head-only 200k variant.
Relabeling the policy year from 2018 to 2031 leaves selected answers unchanged in 12 paired tests per model; separate outcome-note interventions reveal task-dependent information sensitivity. Paired policy results subtract separately simulated baseline paths from tax-cut paths.
The 56-page paper presents learned decision policies as another route to large-scale heterogeneous-household modeling.
Article: https://huggingface.co/blog/ya9919/s2-mas
Updated PDF: https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf
Paper + source release: https://github.com/KataDavidXD/s2-mas/releases/tag/v67
Models: https://huggingface.co/collections/ya9919/s-mas-models-sharp
Updated 28 October 2026. Research contact: yl9919@connect.hku.hk.
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