{"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 reducing the precision or data type used to represent the model's weights and activations. In traditional neural networks, weights and activations are typically represented as floating-point numbers with a high degree of precision","score":8.0},{"input":"How does KV cache work in transformers?","response":"KV (Key-Value) caching is a technique used in transformer architectures to improve the performance of language models by reducing the computational cost of accessing embeddings. Here's how it works:\n\nIn traditional transformer models, the model's parameters are stored in memory and can be expensive ","score":8.0},{"input":"What is the difference between GGUF and safetensors?","response":"GGUF (Gradient Guided Uniformly Fixed) and SAFETENSOIRS (SAFETY-Tensor Norms for Efficient Training of Neural Networks under Uncertainty), also known as Safetensoirs (SafTenSoiRS), are both techniques used to optimize the training of deep neural networks. However, they serve different purposes and h","score":7.0}]