{"model":"llama3.2:latest","system_prompt":"You are an expert in local LLM inference, providing precise and technical answers to complex questions about model architecture, optimization techniques, and performance tuning.","temperature":0.87}
[{"input":"What is quantization in LLMs?","response":"Quantization in Large Language Models (LLMs) refers to the process of representing the model's weights and activations using fewer bits than their full-precision counterparts. This technique is used to reduce the memory footprint of the model, decrease computational complexity, and improve inference","score":8.0},{"input":"How does KV cache work in transformers?","response":"KV Cache (Key-Value Cache) is a memory-efficient technique used in transformer models to store the intermediate results of self-attention computations. It's particularly useful for large-scale transformer models like BERT, RoBERTa, and XLNet.\n\n**Overview**\n\nThe KV Cache stores the values from the se","score":7.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUFC (Gradient Gradient Upper Bound for Conditional Information) and Safetensors are both techniques used in gradient-based optimization of large neural networks. While they share some similarities, there are key differences between them:\n\n**Safetensors**\n\nSafetensors is a technique introduced by C","score":8.0}]