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README.md

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 The importance of a small parameter large language model (LLM) lies in its ability to balance performance and efficiency. As LLMs grow increasingly sophisticated, the trade-off between model size and computational resource demands becomes critical. A smaller parameter model offers significant advantages, such as reduced memory usage, faster inference times, and lower energy consumption, all while retaining a high level of accuracy and contextual understanding. These models are particularly valuable in real-world applications where resources like processing power and storage are limited, such as on mobile devices, edge computing, or low-latency environments.
 
 ## Llama 3.2 Chibi 3B
-This experimental model is the result from continual pre-training of [Meta's Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on a small mixture of japanese datasets.
+This experimental model is a result from continual pre-training of [Meta's Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on a small mixture of japanese datasets.
 
 ## Architecture
 [Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B)