{"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 numbers) to smaller integer types, such as 8-bit or 16-bit integers. This technique is used to re","score":6.0},{"input":"How does KV cache work in transformers?","response":"KV Cache is a technique used in transformer models to improve their performance by reducing the computational complexity of matrix multiplications. In traditional dense attention mechanisms, the attention weights are computed using a softmax function over the query-key (QK) dot product. However, thi","score":6.0},{"input":"What is the difference between GGUF and safetensors?","response":"A question that gets to the heart of advanced optimization techniques!\n\nGGUf (Gradient Gradients Unrolled) and Safetensors are two different approaches used in deep learning model optimization. While both aim to reduce the impact of gradients on the training process, they differ in their underlying ","score":8.0}]