---

library_name: transformers
license: other

---

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)

# QuantFactory/sarvam-2b-v0.5-GGUF
This is quantized version of [sarvamai/sarvam-2b-v0.5](https://huggingface.co/sarvamai/sarvam-2b-v0.5) created using llama.cpp

# Original Model Card


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 4 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.

sarvam-2b will be trained on a data mixture containing equal parts English (2T) and Indic (2T) tokens. The current checkpoint has seen a total of 2 trillion tokens, and has not undergone any post-training.

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 की बेटी इंदिरा गांधी थीं।\n\n'
```

More technical details like evaluations and benchmarking will be posted soon.