|
--- |
|
library_name: transformers |
|
license: other |
|
--- |
|
|
|
**The fully-trained version of this model is now available at https://huggingface.co/sarvamai/sarvam-1** |
|
|
|
Update (Aug 15, 2024): You can now get started with text completions and supervised finetuning using [this notebook](https://colab.research.google.com/drive/1IZ-KJgzRAMr4Rm_-OWvWwnfTQwRxOknp?usp=sharing) on Google colab! |
|
|
|
This is an early checkpoint of `sarvam-2b`, a small, yet powerful language model pre-trained from scratch on 2 trillion tokens. It is trained to be good at 10 Indic languages + English. Officially, the Indic languages supported are: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, and Telugu. |
|
|
|
The final checkpoint of `sarvam-2b` will be released soon, and it will be trained on a data mixture of 4 trillion tokens: containing equal parts English (2T) and Indic (2T) tokens. |
|
|
|
The current checkpoint has not undergone any post-training. You can see the capabilities of the current checkpoint in [this video](https://www.youtube.com/watch?v=DFtAS1BCKvk). |
|
|
|
The model was trained with [NVIDIA NeMo™ Framework](https://github.com/NVIDIA/NeMo) on the Yotta Shakti Cloud using HGX H100 systems. |
|
|
|
Getting started: |
|
``` |
|
from transformers import pipeline |
|
pipe = pipeline(model='sarvamai/sarvam-2b-v0.5', device=0) |
|
pipe('भारत के प्रथम प्रधानमंत्री', max_new_tokens=15, temperature=0.1, repetition_penalty=1.2)[0]['generated_text'] |
|
# 'भारत के प्रथम प्रधानमंत्री जवाहरलाल नेहरू थे।\n\n' |
|
``` |
|
|
|
## Tokenizer |
|
|
|
`sarvam-2b`'s tokenizer is built to be efficient for Indic languages and has an average fertility score of ~2 which is significantly lower than other models. |
|
|
|
Here is a comparison of fertility scores between `sarvam-2b` and other popular models. |
|
| |Sarvam-2B|Llama-3.1|Gemma-2|GPT-4o| |
|
|--------|------|---------|-------|------| |
|
|ben_Beng|2.07 |8.02 |3.72 |2.34 | |
|
|eng_Latn|1.43 |1.24 |1.23 |1.23 | |
|
|guj_Gujr|1.81 |9.97 |3.9 |2.3 | |
|
|hin_Deva|1.4 |2.67 |1.96 |1.65 | |
|
|kan_Knda|2.37 |14.95 |5.55 |3.29 | |
|
|mal_Mlym|2.85 |16.26 |5.88 |3.52 | |
|
|mar_Deva|1.77 |3.99 |3.2 |2.56 | |
|
|ory_Orya|2.35 |16.84 |6.87 |6.83 | |
|
|pan_Guru|1.68 |8.19 |3.37 |2.72 | |
|
|tam_Taml|2.17 |12.39 |4.19 |3.17 | |
|
|tel_Telu|2.14 |13.3 |4.57 |3.06 | |
|
|**Average** |**2.08** |**9.34** |**4.01** |**3.00** | |
|
|
|
More technical details like evaluations and benchmarking will be posted soon. |
