{"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 model weights and activations using a smaller number of bits, typically 8 or 16-bit integers, instead of the traditional floating-point representations used in traditional neural networks. This technique aims to reduc","score":6.0},{"input":"How does KV cache work in transformers?","response":"KV Cache is a technique used in self-attention mechanisms of transformer models, particularly in BERT and its variants. It's an optimization technique that stores the key (K), value (V), and query (Q) embeddings in a separate cache instead of computing them from scratch during inference.\n\nThe KV Cac","score":8.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUf (Gradual Gradient Undo Fusing) and safetensors are both techniques used to improve the stability and robustness of neural networks during training. While they share some similarities, there are key differences between them:\n\n1. Purpose:\n   - GGUf is primarily designed to mitigate the vanishing ","score":8.0}]