Quantisation

Explaining the concept of K/V context cache quantisation, why it matters and the journey to integrate it into Ollama. Why K/V Context Cache Quantisation Matters The introduction of K/V context cache quantisation in Ollama is significant, offering users a range of benefits: • Run Larger Models: With reduced VRAM demands, users can now run larger, more powerful models on their existing hardware. • Expand Context Sizes: Larger context sizes allow LLMs to consider more information, leading to potentially more comprehensive and nuanced responses. For tasks like coding, where longer context windows are beneficial, K/V quantisation can be a game-changer. • Reduce Hardware Utilisation: Freeing up memory or allowing users to run LLMs closer to the limits of their hardware. Running the K/V context cache at Q8_0 quantisation effectively halves the VRAM required for the context compared to the default F16 with minimal quality impact on the generated outputs, while Q4_0 cuts it down to just one third (at the cost of some noticeable quality reduction). ...

Code, Chaos, and Copilots is a talk I gave in July 2024 as an intro to how I use AI/LLMs to augment my capabilities every day. What I use AI/LLMs for Prompting tips Codegen workflow Picking the right models Model formats Context windows Quantisation Model servers Inference parameters Clients & tools Getting started cheat-sheets Download Slide Deck Disclaimer I’m not a ML Engineer or data scientist, As such, the information presented here is based on my understanding of the subject and may not be 100% accurate or complete. ...

AI models (“LLMs” in this case) have inherently large sizes and computational requirements that often pose challenges for deployment and use. ...