{"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. Always ensure your responses are detailed and include relevant examples or references where applicable.","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 the traditional 32-bit floating-point representation to a lower, more energy-efficient data type. The goal of quantization is to reduce the memory footprint of the","score":8.0},{"input":"How does KV cache work in transformers?","response":"KV Cache (Key-Value Cache) is a technique used in transformers to improve the efficiency of the self-attention mechanism. In this answer, we'll dive into how KV cache works, its benefits, and provide an example implementation.\n\n**What is the problem with traditional self-attention?**\n\nIn transformer","score":8.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUf (Gradient Guided Uncertainty for Federated Optimization) and Safetensors are two different approaches to mitigate the issues associated with model updates in federated optimization, which is a distributed machine learning paradigm where multiple clients contribute to training a global model.\n\n*","score":6.0}]