Dive into an 11-minute technical video exploring the groundbreaking Mistral 7B language model and its innovative architectural improvements. Learn about the key features that make this open-source model outperform its competitors, including Grouped-query attention (GQA), Sliding Window Attention (SWA), Rolling Buffer Cache, and Pre-fill and Chunking techniques. Explore detailed comparisons with LLAMA 2 and code LLAMA, understand the instruction finetuning process, and examine LLM boxing concepts. Follow along with a machine learning researcher's comprehensive breakdown of the technical paper, complete with visual explanations and practical insights into the model's superior speed and efficiency characteristics.
Mistral 7B - Understanding the Architecture and Performance Improvements
Overview
Syllabus
- Intro
- Sliding Window Attention SWA
- Rolling Buffer Cache
- Pre-fill and Chunking
- Results
- Instruction Finetuning
- LLM boxing
- Conclusion
Taught by
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