{"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, performance tuning, and deployment strategies.","temperature":0.85}
[{"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 (FP32) numbers to lower-precision data types, typically integers (e.g., FP16 or INT8). This technique aims to achieve several benefits:\n\n1.  **Memor","score":8.0},{"input":"How does KV cache work in transformers?","response":"KV Cache is a crucial component in transformer-based architectures, particularly in those that use the Transformer-XL or DistilBERT models. It's a caching mechanism designed to optimize the self-attention layer of the transformer model by precomputing and storing attention weights for key-value pair","score":8.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGU (Graph-Approximate Nearest Neighbor) Union Fusion and Safetensors are two different concepts in the context of efficient neural network inference, particularly for deep learning models. While they share some similarities, they have distinct differences:\n\n**GGU (Graph-Approximate Nearest Neighbor","score":8.0}]