{"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 reducing the precision of the model's weights and activations from the native floating-point data type (typically 32-bit or 64-bit) to a lower precision data type, such as integers or binary numbers.\n\nThere are two primary types o","score":7.0},{"input":"How does KV cache work in transformers?","response":"KV Cache is a technique used in transformer models to improve performance by caching the key-value pairs during self-attention. Here's a detailed explanation of how it works:\n\n**Problem Statement:**\nIn transformer models, the attention mechanism requires computing the dot product between query (Q) a","score":6.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUFA (Gaussian Gradient Undo for Federated Learning) and Safetensors are two different techniques used to mitigate the effects of differential privacy in federated learning (FL). While both methods aim to provide robustness against adversarial attacks, they differ in their approach and implementati","score":7.0}]