Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggerganov/llama.cpp#5962
In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.
See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix
| conda create --name myenv python=3.10.11 | |
| conda activate myenv | |
| conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia | |
| pip install xformers | |
| pip install audiocraft | |
| pip install IPython |
| from IPython import display as ipd | |
| from audiocraft.models import musicgen | |
| from audiocraft.utils.notebook import display_audio | |
| import torch | |
| import torchaudio | |
| import os | |
| # Define the output directory | |
| output_dir = r'C:\Users\USER\OneDrive\Desktop\musicgen2' |
| import torch | |
| print(torch.version.cuda) | |
| print(torch.cuda.is_available()) |
This project took place over the summer of 2022 as part of the Google Summer of Code, with support from the Intelligent Instruments Lab, Beagleboard Foundation and Bela. The project's objective was to improve the tooling available for those looking to use machine learning models in their Bela projects. For some background, Bela is a maker platform built on top of the Beaglebone Black with a focus on real-time audio and sensor processing for use in interactive art projects including digital instrument design. The availability of machine learning tools to be used with Bela would allow for new design practices incorporating machine learning models.
The original goal of this project arose out of the constraints of Bela's low-powered (in machine learning terms) processor and the real-time constraints of interactive projects. To aid development on this platform there is a need for performance analysis tools that allow for quick evaluation of different models on Bela. This proj
| ###################################################### | |
| # Devtron # | |
| # Kubernetes-Native User-Friendly CI, CD, and GitOps # | |
| # https://youtu.be/ZKcfZC-zSMM # | |
| ###################################################### | |
| # Referenced videos: | |
| # - Argo CD - Applying GitOps Principles To Manage Production Environment In Kubernetes: https://youtu.be/vpWQeoaiRM4 | |
| # - Argo Workflows and Pipelines - CI/CD, Machine Learning, and Other Kubernetes Workflows: https://youtu.be/UMaivwrAyTA | |
| # - Argo Events - Event-Based Dependency Manager for Kubernetes: https://youtu.be/sUPkGChvD54 |
| ###################################################### | |
| # Devtron # | |
| # Kubernetes-Native User-Friendly CI, CD, and GitOps # | |
| # https://youtu.be/ZKcfZC-zSMM # | |
| ###################################################### | |
| # Referenced videos: | |
| # - Argo CD - Applying GitOps Principles To Manage Production Environment In Kubernetes: https://youtu.be/vpWQeoaiRM4 | |
| # - Argo Workflows and Pipelines - CI/CD, Machine Learning, and Other Kubernetes Workflows: https://youtu.be/UMaivwrAyTA | |
| # - Argo Events - Event-Based Dependency Manager for Kubernetes: https://youtu.be/sUPkGChvD54 |
| # Source: https://gist.github.com/6fb3e7da327df9203d9d4c184fcb5831 | |
| ############################################################################## | |
| # Combining Argo CD (GitOps), Crossplane (Control Plane), And Kubevela (OAM) # | |
| # https://youtu.be/eEcgn_gU3SM # | |
| ############################################################################## | |
| # Referenced videos: | |
| # - Argo CD - Applying GitOps Principles To Manage Production Environment In Kubernetes: https://youtu.be/vpWQeoaiRM4 | |
| # - Cloud-Native Apps With Open Application Model (OAM) And KubeVela: https://youtu.be/2CBu6sOTtwk |
| export const App => <DepthCameraComp /> |
| fastlane_version '2.105.2' | |
| before_all do | |
| ensure_git_branch | |
| git_pull | |
| end | |
| platform :ios do | |
| desc 'Fetch certificates and provisioning profiles' | |
| lane :certificates do |
