metadata
license: apache-2.0
datasets:
- gbharti/finance-alpaca
language:
- en
library_name: transformers
tags:
- finance
Fin-RWKV: Attention Free Financal Expert (WIP)
Fin-RWKV is a cutting-edge, attention-free model designed specifically for financial analysis and prediction. Developed as part of a MindsDB Hackathon, this model leverages the simplicity and efficiency of the RWKV architecture to process financial data, providing insights and forecasts with remarkable accuracy. Fin-RWKV is tailored for professionals and enthusiasts in the finance sector who seek to integrate advanced deep learning techniques into their financial analyses.
Features
- Attention-Free Architecture: Utilizes the RWKV (Recurrent Weighted Kernel-based) model, which bypasses the complexity of attention mechanisms while maintaining high performance.
- Lower Costs: 10x to over a 100x+ lower inference cost, 2x to 10x lower training cost
- Tinyyyy: Lightweight enough to run on CPUs in real-time bypassing the GPU - and is able to run on your laptop today
- Finance-Specific Training: Trained on the gbharti/finance-alpaca dataset, ensuring that the model is finely tuned for financial data analysis.
- Transformers Library Integration: Built on the popular 'transformers' library, ensuring easy integration with existing ML pipelines and applications.
Competing Against
| Architecture | Status | Compute Efficiency | Largest Model | Trained Token | Link |
|---|---|---|---|---|---|
| (Fin)RWKV | In Production | O ( N ) | 14B | 500B++ (the pile+) | Paper |
| Ret Net (Microsoft) | Research | O ( N ) | 6.7B | 100B (mixed) | Paper |
| State Space (Stanford) | Prototype | O ( Log N ) | 355M | 15B (the pile, subset) | Paper |
| Liquid (MIT) | Research | - | <1M | - | Paper |
| Transformer Architecture (included for contrasting reference) | In Production | O ( N^2 ) | 800B (est) | 13T++ (est) | - |
Note: Needs more data and training, testing purposes only.