mediocreatbest @ mediocreatbest @lemmy.sdf.org Posts 13Comments 2Joined 2 yr. ago
If a PCI device is completely non-responsive, it's possible to completely remove the device and then re-scan it, hopefully re-initializing the device so it works again.
bduggan/raku-jupyter-kernel allows you to run Raku (né Perl 6) within a Jupyter Notebook environment. In terms of onboarding, this seems to be one of the easiest ways to start using Raku.
Optimizing Deep Learning Models For Raspberry Pi. Custom CNN (on MNIST data) performance from 114ms to 3.75ms. ResNet50 (on "flowers" data): from 1.1s to 1.0s (lowest) or 1.6s (highest).
TinyNeuralNetwork is a library to compress machine learning models through pruning, quantization, and more. Can also convert PyTorch models to TF Lite models.
Overview of machine learning frameworks that are supported on Raspberry Pi: OpenCV, TF Lite, Tencent ncnn, Tencent TNN, Alibaba MNN, Paddle Lite, ARMnn, MXNet + Gluon, PyTorch, and Caffe.
Arm NN is an optimized library of tensor operators for machine learning models to use. Support for TF Lite / ONNX models and Raspberry Pi 4 / armv7.
TextSynth is a hosted service for generating text completions using language models. Free and paid tiers. Could be useful to play with LLMs without a strong computer (Pricing discussion in body text).
LaMini-LM is a collection of small language models that are accessible to run on local hardware without lots of resources. Models range from 250MB to 6.3GB.
jncraton/languagemodels is a simple Python library for running LLMs locally. Supports instruction and embedding use cases. Chooses models according to available RAM.
Altoids tin for watercolor using sculpey modeling clay to create a custom tray for the paints
Taming AI Bots: Prevent LLMs from entering "bad" states using continuous guidance from the LLM ("is this good? bad?") to avoid bad states.