chorous-100m

Chorous1

<p align="center"> <img src="https://img.shields.io/badge/Parameters-100M%20%7C%2050M%20%7C%2027M-brightgreen?style=flat-square" /> <img src="https://img.shields.io/badge/Architecture-Patch--Transformer-purple?style=flat-square" /> <img src="https://img.shields.io/badge/License-8f--ai--license--v1.0-red?style=flat-square" /> </p>

Chorous1 is a suite of three high-performance, patch-based transformer models for multivariate time-series forecasting. Combining RevIN, MAE-style patch masking, and a Flatten Head architecture, Chorous1 delivers state-of-the-art accuracy on real-world benchmark data.

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Table of Contents

• Model Variants
• Architecture
• Quickstart
• Performance
• Limitations
• License

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Model Variants

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Architecture

How It Works

Stage 1 — Neural Encoding. The transformer encoder processes patches of time-series data using RoPE and GQA to capture long-range temporal dependencies and periodic structure.

Stage 2 — RevIN Normalization. A reversible instance normalization layer removes mean and variance shifts from the input prior to processing, then restores them on the output — eliminating the distribution mismatch problem common in real-world deployments.

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Quickstart

import torch
from safetensors.torch import load_file

# Replace "100m" with "50m" or "27m" as needed
weights = load_file("./chorous_checkpoint/100m/model.safetensors")
model.load_state_dict(weights)
model.eval()

# Input shape: [Batch, Channels, Time]
x = torch.randn(1, 7, 512)

with torch.no_grad():
    forecast = model(x)  # Output shape: [1, 7, 96]

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Performance

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Limitations

• Fixed Forecast Horizon — Optimized for 96-step forecasting. Modifying the output head for longer horizons may reduce accuracy.
• Channel Count Constraint — The RevIN layer is initialized using the maximum channel count from the training suite. Inputs exceeding this limit are not supported out of the box.
• Patch Alignment Requirement — Input context length must be an exact multiple of the patch size (16).

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License

Chorous1 is released under the 8f-ai-license-v1.0. Please review the full terms before use in production or commercial applications.