globalnebula
/

FinoLLama

@@ -4,204 +4,110 @@ library_name: peft
 pipeline_tag: text-generation
 tags:
 - base_model:adapter:meta-llama/Meta-Llama-3-8B
 - lora
 - transformers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
 ### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
 ### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
 ### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
 #### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.17.1

 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