{"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 or bit depth of the model's weights and activations from the native floating-point data type (typically 32-bit or 64-bit single-precision floating-point, i.e., float16 or half-precision floating-point) to lo","score":7.0},{"input":"How does KV cache work in transformers?","response":"KV Cache is a crucial component of many transformer architectures, particularly those that require large memory capacities or have computationally expensive operations. Here's a detailed explanation of how KV Cache works:\n\n**What is KV Cache?**\n\nKV Cache (Key-Value Cache) is a memory caching layer u","score":8.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUF (Graph-Based Uniformly Fixed) and Safetensor are both optimization techniques used for efficient training of large neural networks. While they share some similarities, there are key differences between them:\n\n**GGUF:**\n\n1. **Graph-based approach**: GGUF is a graph-based optimization technique t","score":7.0}]