Update README.md

README.md

@@ -1,12 +1,23 @@
 ---
 library_name: transformers
-tags: []
+language:
+- bn
+- en
+- gu
+- hi
+- kn
+- ml
+- mr
+- or
+- pa
+- ta
+- te
 ---
 
 
-# Sarvam-2B
+# Sarvam-1
 
-Sarvam-2B is a 2-billion parameter language model specifically optimized for Indian languages. It provides best in-class performance in 10 Indic languages (bn, gu, hi, kn, ml, mr, or, pa, ta, te) when compared with popular models like Gemma-2-2B and Llama-3.2-3B. It is also competitive against the much larger models like Llama-3.1-8B in these languages. More details can be found in our [release blog](https://www.sarvam.ai/blogs/sarvam-2b).
+Sarvam-1 is a 2-billion parameter language model specifically optimized for Indian languages. It provides best in-class performance in 10 Indic languages (bn, gu, hi, kn, ml, mr, or, pa, ta, te) when compared with popular models like Gemma-2-2B and Llama-3.2-3B. It is also competitive against the much larger models like Llama-3.1-8B in these languages. More details can be found in our [release blog](https://www.sarvam.ai/blogs/sarvam-1).
 
 The model was trained with  [NVIDIA NeMo™ Framework](https://github.com/NVIDIA/NeMo)  on the Yotta Shakti Cloud using HGX H100 systems.
 
@@ -51,8 +62,8 @@ The model was trained with  [NVIDIA NeMo™ Framework](https://github.com/NVIDIA
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # Load model and tokenizer
-model = AutoModelForCausalLM.from_pretrained("Sarvam/sarvam-2b")
-tokenizer = AutoTokenizer.from_pretrained("Sarvam/sarvam-2b")
+model = AutoModelForCausalLM.from_pretrained("sarvamai/sarvam-1")
+tokenizer = AutoTokenizer.from_pretrained("sarvamai/sarvam-1")
 
 # Example usage
 text = "कर्नाटक की राजधानी है:"