Textbooks Are All You Need: Examples for Section 5
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.
Textbooks Are All You Need: Limitation of Phi-1
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.
Textbooks Are All You Need: Additional Examples for Section 3
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.
Textbooks Are All You Need: Conclusion and References
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.
Textbooks are All You Need: N-gram Overlap and Embedding and Syntax-based Similarity Analysis
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.
Textbooks are All You Need: Data Pruning for Unbiased Performance Evaluation
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.
Textbooks are All You Need: Evaluation on Unconventional Problems With LLM Grading
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.
Textbooks are All You Need: Spikes of Model Capability After Finetuning on CodeExercises
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.
Textbooks are All You Need: Model Architecture and Training
12 Sept 2024
In this study, researchers from Microsoft introduce phi-1, a new large language model for code, with significantly smaller size than competing models.