|
|
--- |
|
|
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 |
|
|
|
|
|
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. |
|
|
|
|
|
--- |
|
|
|
|
|
## Model Details |
|
|
|
|
|
### Model Description |
|
|
|
|
|
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. |
|
|
|
|
|
- **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) |
|
|
|
|
|
|
|
|
## Uses |
|
|
|
|
|
### Direct Use |
|
|
|
|
|
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 |
|
|
|
|
|
### Downstream Use |
|
|
|
|
|
- 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 |
|
|
|
|
|
### Out-of-Scope Use |
|
|
|
|
|
- Licensed financial advice without human review |
|
|
- Predictive trading or speculative financial activities |
|
|
- Processing personally identifiable financial data without consent |
|
|
|
|
|
--- |
|
|
|
|
|
## Bias, Risks, and Limitations |
|
|
|
|
|
- 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. |
|
|
|
|
|
### Recommendations |
|
|
|
|
|
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. |
|
|
|
|
|
--- |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Data |
|
|
|
|
|
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 |
|
|
|
|
|
Data was curated using a **custom financial web crawler** built for regulatory document scraping and normalization. |
|
|
|
|
|
### Training Procedure |
|
|
|
|
|
#### Preprocessing |
|
|
- Data cleaned, tokenized, and formatted into structured “context → reasoning → insight” triplets. |
|
|
- Outliers filtered using statistical anomaly detection. |
|
|
- Financial equations standardized using symbolic formatting. |
|
|
|
|
|
#### 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) |
|
|
|
|
|
#### Speeds, Sizes, Times |
|
|
- Total parameters (trainable): ~120M |
|
|
- Checkpoint size: ~2.5 GB |
|
|
- Average training speed: 420 tokens/sec |
|
|
- Total training time: ~6 hours |
|
|
|
