{"model":"llama3.2:latest","system_prompt":"You are a highly skilled engineer specializing in local LLM inference, providing detailed and accurate technical answers on model architecture, optimization techniques, and performance tuning.","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 numbers (typically 32-bit or 64-bit floats) to lower-precision data types, such as integers or binary digits. The goal of quantization is to reduce ","score":7.0},{"input":"How does KV cache work in transformers?","response":"KV Cache is a technique used in transformer-based models to store the keys (K) and values (V) of the attention mechanism separately from the query (Q), rather than storing them together with the weights. This allows for faster access and retrieval of the cached values, which can lead to significant ","score":7.0},{"input":"What is the difference between GGUF and safetensors?","response":"As an expert in local LLM inference, I'd be happy to explain the differences between GGUF (Graph-Based Unstructured Fine-Tuning) and Safetensors.\n\n**GGUF:**\nGGUF is a graph-based approach for fine-tuning local models. It represents the model as a directed acyclic graph (DAG), where each node corresp","score":7.0}]