{"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":0.9}
[{"input":"What is quantization in LLMs?","response":"Quantization in Large Language Models (LLMs) refers to the process of reducing the precision of the model's weights and activations from floating-point numbers (typically 32-bit or 64-bit Floating Point Representation - FP32/FP64) to lower-precision integers, such as 8-bit integers. This technique i","score":8.0},{"input":"How does KV cache work in transformers?","response":"In transformer architectures, the key-value (KV) cache is a memory-efficient technique used to store intermediate results of self-attention computations. This helps reduce computational complexity by avoiding redundant calculations.\n\nThe KV cache works as follows:\n\n1. **Key-value storage**: During e","score":7.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUf (Gaussian Gradient Upper Bound for Training) and SafeTensors are two different tools used for safe computation in machine learning models, particularly in the context of tensor operations.\n\n**GGUf (Gaussian Gradient Upper Bound for Training)**:\n\nGGUf is a tool that provides an upper bound on th","score":8.0}]