Updated overview section

README.md

@@ -26,9 +26,9 @@ widget:
 # Krutrim-2
 
 ## Model Overview
-Krutrim-2 is a 12B parameter language model developed by the OLA Krutrim team. It is based on the Mistral-NeMo 12B architecture and has undergone continual pretraining with 500B tokens across various domains, including web data, code, math, Indic languages, Indian context data, synthetic data, and books. Following pretraining, the model was finetuned on 1.5M data points covering a diverse range of tasks, including knowledge recall, math, reasoning, coding, safety & non-compliance, instruction following, creative writing, and role-playing.
+Krutrim-2 is a 12B parameter language model developed by the OLA Krutrim team. It is built on the Mistral-NeMo 12B architecture and trained across various domains, including web data, code, math, Indic languages, Indian context data, synthetic data, and books. Following pretraining, the model was finetuned on diverse data covering a wide range of tasks, including knowledge recall, math, reasoning, coding, safety & non-compliance, instruction following and creative writing.
 
-After fine-tuning, the model underwent Direct Preference Optimization (DPO) with 300K data points to enhance alignment across multiple aspects. DPO was applied to improve response helpfulness, safety, and compliance, making the model more robust against harmful prompts, reducing biases, and improving factual consistency.
+After fine-tuning, the model underwent Direct Preference Optimization (DPO) to enhance alignment across multiple aspects. DPO was applied to improve response helpfulness, safety, and compliance, making the model more robust against harmful prompts, reducing biases, and improving factual consistency.
 
 ## Key Features
 - 12B parameter dense transformer model leading to better generalization compared to Krutrim-1 7B;
@@ -50,8 +50,8 @@ After fine-tuning, the model underwent Direct Preference Optimization (DPO) with
 
 | Model Name | Release Date |Release Note | Reference|
 |------------|-------------|-------------|-------------|
-| Krutrim-2-Base-0131   | 2024-01-31  | Continually Pre-trained on MN12B base | [Here](https://huggingface.co/krutrim-ai-labs/Krutrim-2-base-0131)|
-| Krutrim-2-Instruct-0131  | 2024-01-31 | Finetuned and DPOed version of Krutrim-2-Base-0131 |[Here](https://huggingface.co/krutrim-ai-labs/Krutrim-2-instruct-0131)|
+| Krutrim-2-Base-0131   | 2024-01-31  | Continually Pre-trained on MN12B base | [Here](https://huggingface.co/krutrim-ai-labs/Krutrim-2-base)|
+| Krutrim-2-Instruct-0131  | 2024-01-31 | Finetuned and DPOed version of Krutrim-2-Base-0131 |[Here](https://huggingface.co/krutrim-ai-labs/Krutrim-2-instruct)|
 
 
 ## Data Freshness
@@ -126,7 +126,7 @@ To use the model, you can load it with `AutoModelForCausalLM` as follows:
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-model_id = "path/to/Krutrim-2_model"
+model_id = "krutrim-ai-labs/Krutrim-2-instruct"
 
 # Load model and tokenizer
 model = AutoModelForCausalLM.from_pretrained(model_id)