Transformers
crypto
deep-learning
time-series
forecasting
transformer
state-space-models
open-source
scaling-laws
Instructions to use duonlabs/apogee-nano-2.4M-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duonlabs/apogee-nano-2.4M-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("duonlabs/apogee-nano-2.4M-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Apogée: Crypto Market Candlestick Dataset
Overview
Most traders believe crypto is random, but deep learning scaling laws suggest otherwise. Apogée is an open-source research initiative exploring the scaling laws of crypto market forecasting. While financial markets are often assumed to be unpredictable, modern deep learning suggests that increasing data and compute could uncover measurable predictability. Our goal is to quantify how many bits of future price movement can be inferred from historical candlestick data. More informations on Apogée
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