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--- |
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license: apache-2.0 |
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datasets: |
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- gbharti/finance-alpaca |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- finance |
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widget: |
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- text: "Is this headline positive or negative? Headline: Australian Tycoon Forrest Shuts Nickel Mines After Prices Crash." |
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example_title: "Sentiment analysis" |
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- text: "Aluminum price per KG is 50$. Forecast max: +1$ min:+0.3$. What should be the current price of aluminum?" |
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example_title: "Forecast" |
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--- |
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# Fin-RWKV: Attention Free Financal Expert (WIP) |
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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. |
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## Use Cases |
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- Sentiment analysis |
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- Forecast |
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- Product Pricing |
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## Features |
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- Attention-Free Architecture: Utilizes the RWKV (Recurrent Weighted Kernel-based) model, which bypasses the complexity of attention mechanisms while maintaining high performance. |
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- Lower Costs: 10x to over a 100x+ lower inference cost, 2x to 10x lower training cost |
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- Tinyyyy: Lightweight enough to run on CPUs in real-time bypassing the GPU - and is able to run on your laptop today |
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- Finance-Specific Training: Trained on the gbharti/finance-alpaca dataset, ensuring that the model is finely tuned for financial data analysis. |
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- Transformers Library Integration: Built on the popular 'transformers' library, ensuring easy integration with existing ML pipelines and applications. |
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## Competing Against |
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| Name | Param Count | Cost | Inference Cost | |
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|---------------|-------------|------|----------------| |
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| Fin-RWKV | 169M | $1.45 | Free on HuggingFace 🤗 & Low-End CPU | |
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| [BloombergGPT](https://www.bloomberg.com/company/press/bloomberggpt-50-billion-parameter-llm-tuned-finance/) | 50 Billion | $1.3 million | Enterprise GPUs | |
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| [FinGPT](https://huggingface.co/FinGPT) | 7 Bilion | $302.4 | Consumer GPUs | |
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| Architecture | Status | Compute Efficiency | Largest Model | Trained Token | Link | |
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|--------------|--------|--------------------|---------------|---------------|------| |
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| (Fin)RWKV | In Production | O ( N ) | 14B | 500B++ (the pile+) | [Paper](https://arxiv.org/abs/2305.13048) | |
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| Ret Net (Microsoft) | Research | O ( N ) | 6.7B | 100B (mixed) | [Paper](https://arxiv.org/abs/2307.08621) | |
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| State Space (Stanford) | Prototype | O ( Log N ) | 355M | 15B (the pile, subset) | [Paper](https://arxiv.org/abs/2302.10866) | |
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| Liquid (MIT) | Research | - | <1M | - | [Paper](https://arxiv.org/abs/2302.10866) | |
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| Transformer Architecture (included for contrasting reference) | In Production | O ( N^2 ) | 800B (est) | 13T++ (est) | - | |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/631ea4247beada30465fa606/7vAOYsXH1vhTyh22o6jYB.png" width="500" alt="Inference computational cost vs. Number of tokens"> |
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_Note: Needs more data and training, testing purposes only._ |
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