minLoRA
tsai
| minLoRA | tsai | |
|---|---|---|
| 3 | 4 | |
| 389 | 4,730 | |
| - | 3.0% | |
| 2.4 | 7.4 | |
| 11 months ago | 21 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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minLoRA
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[D] Is it possible to train the same LLM instance on different users' data?
This repository seems to be doing it. Basically, you want to take the weights/biases that were trained during the LoRA training process and include them in the compute graph for the larger network, or remove them.
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[P] minLoRA: An Easy-to-Use PyTorch Library for Applying LoRA to PyTorch Models
Theirs requires you to rewrite the whole model and replace every layer you want to apply LoRA to to the LoRA counterpart, or use monky-patching. Mine utilizes PyTorch parametrizations to inject the LoRA logic to existing models. If your model has nn.Linear, you can call add_lora(model) to add LoRA to all the linear layers. And it's not limited to Linear, you can see how I extended it to Embedding, Conv2d in a couple lines of code. https://github.com/cccntu/minLoRA/blob/main/minlora/model.py
tsai
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Aeon: A unified framework for machine learning with time series
Also https://github.com/timeseriesAI/tsai
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What is the current state-of-art in sequence classification?
You might be interested in tsai. I am not affiliated with them and have not used tsai, but I have been planning to try it for too long … well :p
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
how about tsai?
- Machine learning with Time series data
What are some alternatives?
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
GTSRB - Convolutional Neural Network for German Traffic Sign Recognition Benchmark
sktime-dl - DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
nixtla - TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
mlforecast - Scalable machine 🤖 learning for time series forecasting.
gluonts - Probabilistic time series modeling in Python
dspytai - EVMOS blockchain Dapp that utilizes on-chain data to model potential price fluctuations in real-time from covalent api.
TGLSTM - Pytorch implementation of LSTM for irregular time series