| --- |
| base_model: meta-llama/Meta-Llama-3-8B |
| library_name: peft |
| pipeline_tag: text-generation |
| tags: |
| - base_model:adapter:meta-llama/Meta-Llama-3-8B |
| - finoai |
| - lora |
| - transformers |
| - financial-analysis |
| - privacy-ai |
| license: mit |
| language: |
| - en |
| --- |
| |
| # Model Card for FinoAI — Financial Intelligence LLM |
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| FinoAI is a privacy-first, explainable financial reasoning model fine-tuned on the Meta-Llama-3-8B base using parameter-efficient fine-tuning (PEFT) and LoRA adapters. |
| It acts as a secure, autonomous AI financial advisor capable of forecasting, anomaly detection, and policy-grounded recommendations across personal and enterprise finance contexts. |
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| --- |
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| ## Model Details |
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| ### Model Description |
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| FinoAI is a hybrid AI model that integrates Graph Neural Ordinary Differential Equations (GNN-ODEs) with a multi-stage Large Language Model reasoning pipeline (Planner → Executor → Fact-Guard). |
| The model performs continuous-time financial forecasting, investment planning, and anomaly detection while maintaining user privacy through federated learning and differential privacy mechanisms. |
| It is designed for both consumer (B2C) and enterprise (B2B) deployment scenarios, supporting API, web, and voice-based interfaces. |
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| - **Developed by:** S Kunal Achintya Reddy |
| - **Model Type:** Financial reasoning and forecasting LLM |
| - **Languages:** English |
| - **License:** MIT |
| - **Fine-tuned from model:** meta-llama/Meta-Llama-3-8B |
| - **Frameworks:** PyTorch, PEFT, Hugging Face Transformers, LangChain |
| - **Version:** 1.0 (October 2025) |
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| ## Uses |
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| ### Direct Use |
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| FinoAI can be used directly for: |
| - Personalized financial advisory and planning |
| - Debt optimization and anomaly detection |
| - Investment forecasting and policy compliance queries |
| - Conversational financial assistants or embedded fintech copilots |
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| ### Downstream Use |
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| - Finetuning or domain adaptation for specific markets (e.g., insurance, SME credit scoring) |
| - Embedding as a reasoning layer in enterprise fintech dashboards |
| - Integration with federated finance apps requiring privacy guarantees |
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| ### Out-of-Scope Use |
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| - Licensed financial advice without human review |
| - Predictive trading or speculative financial activities |
| - Processing personally identifiable financial data without consent |
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| --- |
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| ## Bias, Risks, and Limitations |
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| - FinoAI’s outputs depend on data quality and may reflect inaccuracies in the financial documents used for training. |
| - The model is not a certified financial advisor and should be used as a decision-support tool. |
| - While differential privacy mitigates leakage risk, outputs should not be used for regulated decision-making without compliance oversight. |
| - Model performance may degrade in underrepresented financial systems or local languages. |
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| ### Recommendations |
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| Users and developers should: |
| - Use the model for advisory and educational purposes, not regulatory or transactional decision-making. |
| - Ensure interpretability modules (Fact-Guard and RAG explainability) remain active during deployment. |
| - Periodically retrain with updated financial datasets to avoid model drift. |
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| --- |
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| ## Training Details |
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| ### Training Data |
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| The model was trained on a curated proprietary dataset of: |
| - Publicly available financial documents (RBI guidelines, SEBI reports, OECD datasets) |
| - Educational finance materials (tax codes, investment fundamentals, risk management data) |
| - Synthetic dialogues and case studies generated using reinforcement-based reasoning for advisor simulation |
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| Data was curated using a **custom financial web crawler** built for regulatory document scraping and normalization. |
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| ### Training Procedure |
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| #### Preprocessing |
| - Data cleaned, tokenized, and formatted into structured “context → reasoning → insight” triplets. |
| - Outliers filtered using statistical anomaly detection. |
| - Financial equations standardized using symbolic formatting. |
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| #### Training Hyperparameters |
| - **Base model:** Meta-Llama-3-8B |
| - **Fine-tuning:** LoRA (r=32, alpha=16) |
| - **Batch size:** 64 |
| - **Learning rate:** 2e-4 |
| - **Optimizer:** AdamW |
| - **Precision:** bf16 mixed precision |
| - **Epochs:** 5 |
| - **Training cost:** 9.28 USD (RunPod A100, 6.2 GPU-hours) |
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| #### Speeds, Sizes, Times |
| - Total parameters (trainable): ~120M |
| - Checkpoint size: ~2.5 GB |
| - Average training speed: 420 tokens/sec |
| - Total training time: ~6 hours |
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