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README.md

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 Small parameter LLMs are ideal for navigating the complexities of the Japanese language, which involves multiple character systems like kanji, hiragana, and katakana, along with subtle social cues. Despite their smaller size, these models are capable of delivering highly accurate and context-aware results, making them perfect for use in environments where resources are constrained. Whether deployed on mobile devices with limited processing power or in edge computing scenarios where fast, real-time responses are needed, these models strike the perfect balance between performance and efficiency, without sacrificing quality or speed.
 
 ## Llama 3.2 Chibi 3B
-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.
-This is not fine-tuned for chat or dialogue-based tasks. It has been pre-trained for general language modeling purposes and may require additional fine-tuning for specific applications such as conversational agents or other downstream tasks. Users interested in deploying this model for interactive environments should consider further fine-tuning with suitable datasets.
+This experimental model is the result of continuous 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. It is not fine-tuned for chat or dialogue-based tasks. The model has been pre-trained for general language modeling purposes and may require additional fine-tuning for specific applications, such as conversational agents or other downstream tasks. Users interested in deploying this model for interactive environments should consider further fine-tuning with appropriate datasets.
 
 ## Architecture
 [Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B)