The training process involved preprocessing the data to remove noise and inconsistencies, tokenizing the data using a SentencePiece tokenizer, training the model using a masked language modeling objective, and fine-tuning on downstream tasks.
Model Architecture:
Blending of CodeBERT, Codex, T5, SAM, Gemini, and Megatron
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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation
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