| --- |
| base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference |
| library_name: peft |
| --- |
| # CashWise |
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| ### A Multilingual Financial Reasoning Model for South Africa 🇿🇦 |
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| **CashWise** is a finance-specialised language model fine-tuned from **Meta Llama 3.3 70B Instruct** using the [RayNene/CashWise](https://huggingface.co/datasets/RayNene/CashWise) dataset. |
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| The model targets a practical gap in financial AI: models may possess broad financial knowledge but struggle to turn that knowledge into **clear, safe, context-aware guidance for users outside English-first financial environments**. |
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| CashWise combines large-model reasoning with multilingual financial adaptation across **English, isiZulu, isiXhosa, Setswana, Xitsonga, and Afrikaans**, covering investing, savings, credit, debt, insurance, tax, banking, retirement, fraud, mortgages, business finance, and equity. |
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| ## Model |
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| - **Base model:** Meta Llama 3.3 70B Instruct |
| - **Fine-tuning dataset:** [RayNene/CashWise](https://huggingface.co/datasets/RayNene/CashWise) |
| - **Domain:** Financial reasoning and financial Q&A |
| - **Languages:** 6 South African languages |
| - **Focus:** Financial reasoning, safety, multilingual communication, and readability |
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| ## Evaluation Protocols |
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| CashWise was evaluated using a held-out evaluation set designed to measure whether domain adaptation improved financial performance without relying solely on training-set examples. |
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| Evaluation was conducted across four dimensions: |
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| ### 1. Financial Reasoning |
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| The model was tested on unseen financial questions covering: |
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| - Investing |
| - Savings |
| - Credit and debt |
| - Insurance |
| - Tax |
| - Banking |
| - Retirement |
| - Business finance |
| - Equity and compensation |
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| Responses were compared against expected answers using financial QA and reasoning evaluation criteria. |
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| ### 2. Financial Benchmark Evaluation |
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| The evaluation protocol was aligned with established financial NLP benchmarks, including: |
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| - **FinQA** — numerical and logical reasoning over financial information |
| - **FinanceBench** — open-book financial question answering |
| - **FinBen** — broader financial evaluation covering reasoning, QA, risk, and decision-making |
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| The same evaluation methodology was applied to the base model and the fine-tuned CashWise model to measure the effect of domain adaptation. |
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| ### 3. Multilingual Evaluation |
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| The model was evaluated across the six languages represented in the training data to assess: |
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| - Semantic preservation |
| - Financial terminology |
| - Response relevance |
| - Natural language quality |
| - Cross-lingual consistency |
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| Particular attention was given to whether financial concepts remained intact rather than being translated literally. |
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| ### 4. Readability & Safety |
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| Generated responses were additionally evaluated for: |
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| - Clarity |
| - Unnecessary financial jargon |
| - Appropriate uncertainty |
| - Risk disclosure |
| - Avoidance of guaranteed financial outcomes |
| - Distinction between general financial information and personalised advice |
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| ## Results |
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| Fine-tuning produced a substantial improvement over the underlying Llama 3.3 70B Instruct model. |
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| | Metric | Base Llama 3.3 70B | CashWise | Change | |
| |---|---:|---:|---:| |
| | Financial evaluation win rate | **<15%** | **87%** | **+72pp** | |
| | Financial reasoning | Baseline | Improved | ↑ | |
| | Financial QA | Baseline | Improved | ↑ | |
| | Multilingual financial response | Baseline | Improved | ↑ | |
| | Readability | Baseline | Improved | ↑ | |
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| ### Financial Win Rate |
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| The headline result is the increase in financial evaluation win rate from **under 15% for the base model to 87% after CashWise fine-tuning**. |
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| This represents an improvement of **more than 5× the original win rate** and demonstrates that targeted financial training can substantially change the behaviour of a large general-purpose model on specialised financial tasks. |
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| ### Readability |
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| The underlying CashWise dataset also demonstrated a measurable improvement in response readability: |
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| | Response type | Score | Grade | |
| |---|---:|---| |
| | Original / community responses | **5.0** | D | |
| | Adaptive responses | **7.3** | B | |
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| The **2.3-point improvement** reflects the dataset's emphasis on converting financial explanations into clearer, more accessible responses. |
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| ## Key Finding |
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| The results suggest that **domain-specific data can substantially improve the financial capabilities of a large general-purpose model without requiring training a foundation model from scratch**. |
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| CashWise therefore provides both: |
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| 1. A specialised dataset for multilingual financial reasoning. |
| 2. A 70B-parameter model demonstrating the effect of targeted domain adaptation. |
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| > **From <15% to 87% financial evaluation win rate.** |
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| ## Intended Use |
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| CashWise can be used for: |
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| - Financial QA and reasoning |
| - Multilingual financial assistants |
| - Financial NLP research |
| - African-language model evaluation |
| - Domain-specific LLM fine-tuning |
| - Research into financial accessibility and AI safety |
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| ## Limitations |
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| CashWise is a research model and **is not a substitute for a licensed financial, tax, or investment professional**. |
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| Financial regulations, tax rules, products, and individual circumstances change. Model outputs should therefore be independently verified before being used for consequential financial decisions. |
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| ## Dataset |
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| The model was trained using: |
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| **[RayNene/CashWise](https://huggingface.co/datasets/RayNene/CashWise)** |
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| CashWise is a multilingual financial Q&A dataset developed as part of the **Adaption Labs AutoScientist Challenge**. |
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| ## Acknowledgements |
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| Developed as part of the **Adaption Labs AutoScientist Challenge**, exploring how targeted datasets and domain adaptation can improve frontier language models on specialised tasks. |
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| ## Citation |
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| ```bibtex |
| @misc{cashwise2026, |
| title = {CashWise: A Multilingual Financial Reasoning Model for South Africa}, |
| author = {Nene, Ray}, |
| year = {2026}, |
| url = {https://huggingface.co/RayNene/CashWise} |
| } |
