#python #asynchronous_programming #asyncio #deep_learning #deep_neural_networks #dht #distributed_systems #distributed_training #hivemind #mixture_of_experts #pytorch #volunteer_computing

https://github.com/learning-at-home/hivemind

#python #billion_parameters #compression #data_parallelism #deep_learning #gpu #inference #machine_learning #mixture_of_experts #model_parallelism #pipeline_parallelism #pytorch #trillion_parameters #zero

DeepSpeed is a powerful tool for training and using large artificial intelligence models quickly and efficiently. It allows you to train models with billions or even trillions of parameters, which is much faster and cheaper than other methods. With DeepSpeed, you can achieve significant speedups, reduce costs, and improve the performance of your models. For example, it can train ChatGPT-like models 15 times faster than current state-of-the-art systems. This makes it easier to work with large language models without needing massive resources, making AI more accessible and efficient for everyone.

https://github.com/microsoft/DeepSpeed

#c_lang #avx2 #c99 #cpu_inference #deep_learning #from_scratch #inference_engine #kimi_k3 #linear_attention #llm #llm_inference #machine_learning #memory_efficient #mixture_of_experts #moe #mxfp4 #quantization #simd #systems_programming #transformer #zero_dependencies

kimi-k3-in-c runs the Kimi K3 language model on a CPU without a GPU or machine learning framework. It supports text generation and multi-turn text chat on Linux, macOS, and Windows. By loading model data from disk as needed, it can run the full model with limited memory, making it useful on machines that cannot hold the model in RAM. It requires roughly 1.7 TB of free storage, and generation can be slow, especially with less memory or slower disks.

https://github.com/FareedKhan-dev/kimi-k3-in-c