Instructions to use udgamlabs/Vemana-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use udgamlabs/Vemana-Checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("udgamlabs/Vemana-Checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
UDGAM LABS · Training checkpoints · Vemana, 278M parameters, Telugu
Vemana-Checkpoints
Twelve checkpoints from the training run of Vemana-Base, a 278M Telugu
language model trained from scratch on 12.0B token positions (one H100, about 16 hours, 26-27 October 2026, live at
udgamlabs.com/live/vemana). Each folder is a complete Hugging Face model
(Gemma4ForCausalLM, float32) with the tokenizer, so any of them loads on its own:
from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("udgamlabs/Vemana-Checkpoints", subfolder="m050-step-011444")
model = AutoModelForCausalLM.from_pretrained("udgamlabs/Vemana-Checkpoints", subfolder="m050-step-011444")
| Folder | Step | Token positions | Fresh-text bits per byte (416 pages, Sep-Oct 2026) |
|---|---|---|---|
m010-step-002289 |
2,289 | 1.2B | 0.420 |
m020-step-004578 |
4,578 | 2.4B | 0.414 |
stable-025-step-005722 |
5,722 | 3.0B | 0.405 |
gate-step-005722 |
5,722 | 3.0B | 0.383 (a copy cooled down from step 4,578, scored as a finished model) |
m030-step-006866 |
6,866 | 3.6B | 0.406 |
m040-step-009155 |
9,155 | 4.8B | 0.400 |
m050-step-011444 |
11,444 | 6.0B | 0.398 |
m060-step-013733 |
13,733 | 7.2B | 0.398 |
m070-step-016022 |
16,022 | 8.4B | 0.398 |
m080-step-018310 |
18,310 | 9.6B | 0.393 (start of the final cool-down) |
m090-step-020599 |
20,599 | 10.8B | 0.371 |
final-step-022888 |
22,888 | 12.0B | 0.361 (= Vemana-Base) |
The scores use the same script and pages as the Vemana-Base card (lower is better). Most of the gain comes in the cool-down: the stable checkpoints level off near 0.398 between 40% and 70% of the run, and the last 20% brings the score to 0.361.
Each folder also holds fresh.json (the per-page scores behind the table) and train_state.json (the run's step,
schedule settings and the text it wrote at that point).
Licence
Apache-2.0. Copyright 2026 Udgam Labs LLP. Training data credits are in the
Vemana-Base NOTICE.
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
