{"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 representing model weights and activations using a limited number of bits instead of the traditional 32-bit floating-point precision. This technique aims to reduce the memory footprint, computational complexity, and dynamic range ","score":8.0},{"input":"How does KV cache work in transformers?","response":"KV Cache is a technique used in transformer models to optimize memory usage and improve computational efficiency. In the context of transformers, KV Cache refers to the cache of key-value pairs that are not present in the embedding layer's embedding matrix.\n\nHere's how it works:\n\n1. **Key-Value Pair","score":8.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUFS (Gradient Guided Unbiased Finite Support) and SafetyTensors are both safety measures used in various deep learning models, particularly for reinforcement learning (RL) and adversarial training. While they share some similarities, there are key differences between the two:\n\n1. Purpose:\n   - **G","score":8.0}]