{"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 reducing the precision of the model's weights and activations from floating-point numbers (typically 32-bit or 64-bit floating point numbers) to lower-precision data types, such as integers.\n\nThere are two primary forms of quantiz","score":8.0},{"input":"How does KV cache work in transformers?","response":"KV Cache (Key-Value Cache) is a technique used in transformer models to efficiently store and retrieve intermediate results during inference. It's an essential component of many modern transformer architectures, including BERT, RoBERTa, and DistilBERT, among others.\n\n**What is KV Cache?**\n\nIn tradit","score":8.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUFC (Gradient Guided Uncertainty Forcing) and SafeTensors are both techniques used to improve the robustness of machine learning models, particularly in the context of uncertainty estimation and out-of-distribution (OOD) detection. While they share some similarities, there are key differences betw","score":8.0}]