#CPU inference

Learn how to deploy open-source LLMs such as Llama 2 on CPUs for effective document Q&A in a privacy compliant manner. Utilize tools including C Transformers, GGML, and LangChain to efficiently manage resources, minimizing reliance on expensive GPU usage. The project provides detailed guidance on local CPU inference from setup to query execution, offering a solution that respects data privacy and avoids third-party dependencies.

ChatGLM.cpp provides a C++ implementation for real-time interactions with models like ChatGLM-6B across diverse hardware, including GPUs from NVIDIA and Apple Silicon. It features memory-efficient quantization, optimized KV caching, and supports finetuned models like P-Tuning v2 and LoRA across Linux, MacOS, and Windows. Python bindings, web demos, and different chat modes are also supported. BLAS and GPU accelerators such as CUDA and Metal optimize performance. Installation through PyPI enhances accessibility.