anbox-playstore-installer
axolotl
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| anbox-playstore-installer | axolotl | |
|---|---|---|
| 3 | 29 | |
| 652 | 5,641 | |
| - | 23.6% | |
| 0.0 | 9.8 | |
| over 1 year ago | 3 days ago | |
| Shell | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
anbox-playstore-installer
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Add arm support to Anbox
https://zhsj.me/blog/view/anbox-and-houdini https://github.com/geeks-r-us/anbox-playstore-installer/blob/master/install-houdini-only.sh https://dev.to/sbellone/how-to-install-anbox-on-debian-1hjd
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Do you think Ubuntu will support android apps natively like Windows 11 is going to ?
https://github.com/geeks-r-us/anbox-playstore-installer
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starting a native adaptive Linux client for Signal
I have not done this, but I gather than installation of microG, GApps and the Google Play Store in Anbox is possible: https://github.com/geeks-r-us/anbox-playstore-installer
axolotl
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Ask HN: Most efficient way to fine-tune an LLM in 2024?
The approach I see used is axolotl with QLoRA using cloud GPUs which can be quite cheap.
https://github.com/OpenAccess-AI-Collective/axolotl
- FLaNK AI - 01 April 2024
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LoRA from Scratch implementation for LLM finetuning
https://github.com/OpenAccess-AI-Collective/axolotl
- Optimized Triton Kernels for full fine tunes
- Axolotl
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Let’s Collaborate to Build a High-Quality, Open-Source Dataset for LLMs!
One option is to look at what Axolotl uses. They have a list of different dataset formats that they support. They're mostly in JSON with specific field names, so you could start putting a dataset together with a text editor or a JSON editor.
- Axolotl: Streamline fine-tuning of AI models
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Dataset Creation Tools?
You can save that overall set into a json file and load it up as training data in whatever you're using. I'm using axolotl for it at the moment. Though a GUI based option is probably best for the first couple of tries until you get a feel for the options.
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Progress on Reproducing Phi-1/1.5
Looking forward to the results! If it turns out the dataset is reproducible, then it might be a good candidate for ReLora training on axolotl!
What are some alternatives?
GmsCore - Free implementation of Play Services
gpt-llm-trainer
LibreSignal - LibreSignal • The truly private and Google-Free messenger for Android.
signal-cli - signal-cli provides an unofficial commandline, JSON-RPC and dbus interface for the Signal messenger.
libsignal - Home to the Signal Protocol as well as other cryptographic primitives which make Signal possible.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
convergence-components
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
textsecure - TextSecure(signal) client package for Go
LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
axolotl - A Signal compatible cross plattform client written in Go, Rust and Vuejs
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI