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In this paper, we present significant advancements in the pretraining ofMistral 7B, a large-scale language model, using a dataset of 32.6 GB,equivalent to 1.1 billion tokens. We explore the impact of extending thecontext length, releasing models with context lengths of 4096 and 32768 tokens,and further refining performance with a specialized 16384 context lengthinstruction-tuned model, we called it Malaysian Mistral.  Our experiments demonstrate the efficacy of continue pretraining and theinfluence of extended context lengths on Mistral 7B's language understandingcapabilities. Additionally, we release a model specifically tuned with a 16384context length instruction, showcasing its potential for capturing nuancedlanguage intricacies.  Furthermore, our research contributes to the benchmarking of MalaysianMistral against prominent language models, including ChatGPT3.5 and Claude 2.We present compelling results indicating Malaysian Mistral's superiorperformance on Tatabahasa (Malay grammar) test set, particularly whenfine-tuned with instructions.  All models released athttps://huggingface.co/collections/mesolitica/malaysian-mistral-7b-6528f2ec825f4bba46c1700c

Large Malaysian Language Model Based on Mistral for Enhanced Local Language Understanding