Update README.md

README.md

@@ -15,11 +15,11 @@ We democratize financial data for large language models (LLM) at [FinNLP](https:
 
 # Why FinGPT?
 
-1). Finance is highly dynamic. [BloombergGPT](https://arxiv.org/abs/2303.17564) retrains an LLM using a mixed dataset of finance and general data sources, which is too expensive (1.3M GPU hours, a cost of around **$5M**). It is costy to retrain an LLM model every month or every week, so lightweight adaptation is highly favorable in finance. Instead of undertaking a costly and time-consuming process of retraining a model from scratch with every significant change in the financial landscape, FinGPT can be fine-tuned swiftly to align with new data (the cost of adaptation falls significantly, estimated at less than **$300 per training**).
+1). Finance is highly dynamic. [BloombergGPT](https://arxiv.org/abs/2303.17564) retrains an LLM using a mixed dataset of finance and general data sources, which is too expensive (1.3M GPU hours, a cost of around **$5M**). It is costly to retrain an LLM model every month or every week, so lightweight adaptation is highly favorable in finance. Instead of undertaking a costly and time-consuming process of retraining a model from scratch with every significant change in the financial landscape, FinGPT can be fine-tuned swiftly to align with new data (the cost of adaptation falls significantly, estimated at less than **$300 per training**).
 
 2). Democratizing Internet-scale financial data is critical, which should allow timely updates (monthly or weekly updates) using an automatic data curation pipeline. But, BloombergGPT has privileged data access and APIs. FinGPT presents a more accessible alternative. It prioritizes lightweight adaptation, leveraging the strengths of some of the best available open-source LLMs, which are then fed with financial data and fine-tuned for financial language modeling.
 
-3). The key technology is "RLHF (Reinforcement learning from human feedback)", which is missing in BloombergGPT. RLHF enables an LLM model to learn individual preferences (risk-aversion level, investing habits, personalized robo-advisor, etc.), which is the ``secret" ingradient of ChatGPT and GPT4.
+3). The key technology is "RLHF (Reinforcement learning from human feedback)", which is missing in BloombergGPT. RLHF enables an LLM model to learn individual preferences (risk-aversion level, investing habits, personalized robo-advisor, etc.), which is the ``secret" ingredient of ChatGPT and GPT4.
 
 ## FinGPT Demos