Finance Chat Assistant
Model Description
Finance Chat Assistant is a finance-focused conversational AI model created by fine-tuning TinyLlama-1.1B-Chat-v1.0 using QLoRA (Quantized Low-Rank Adaptation) on a finance instruction dataset.
The objective of this project is to improve the model's ability to answer finance-related questions including:
- Diversification
- Mutual Funds
- ETFs
- Asset Allocation
- Inflation
- Risk Management
- Retirement Planning
- Investment Strategies
- SIP Investing
This repository contains LoRA adapter weights only and requires the TinyLlama-1.1B-Chat-v1.0 base model for inference.
Model Details
Base Model
TinyLlama/TinyLlama-1.1B-Chat-v1.0
Fine-Tuning Method
- QLoRA
- PEFT
- LoRA
- TRL SFTTrainer
- 4-bit NF4 Quantization
Author
Kuran (GitHub: kurannnnn)
Demo
A Streamlit-based Finance Chat Assistant application was developed using this model.
Features:
- Finance question answering
- Investment concept explanations
- Educational finance assistant
- Interactive chat interface
Model Sources
Hugging Face Repository
https://huggingface.co/Konnnnnnnn/finance-chat-assistant
Base Model
https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
Project Repository
https://github.com/kurannnnn/Finance-Chat-Assistant
Uses
Direct Use
This model can be used for:
- Finance education
- Investment concept explanations
- Personal finance learning
- Finance chatbots
- Academic projects involving LLM fine-tuning
Downstream Use
The model can be integrated into:
- Streamlit applications
- Educational finance assistants
- Learning platforms
- RAG systems
- Research projects
Out-of-Scope Use
This model should not be used for:
- Professional financial advice
- Tax advice
- Legal advice
- Real-money investment decisions
- Financial planning without human verification
Bias, Risks and Limitations
This model was trained on a limited finance instruction dataset and may:
- Produce inaccurate financial information
- Generate outdated recommendations
- Hallucinate facts
- Provide incomplete investment guidance
Users should verify financial information from trusted sources before making decisions.
Recommendation
This model is intended for educational and research purposes only.
Example
Input
What is asset allocation in a portfolio?
Output
Asset allocation refers to the distribution of investments across different asset classes such as stocks, bonds, and cash. The goal is to balance risk and return according to an investor's objectives, financial goals, and risk tolerance.
How to Get Started
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained(
"TinyLlama/TinyLlama-1.1B-Chat-v1.0"
)
tokenizer = AutoTokenizer.from_pretrained(
"Konnnnnnnn/finance-chat-assistant"
)
model = PeftModel.from_pretrained(
base_model,
"Konnnnnnnn/finance-chat-assistant"
)
Training Details
Dataset
Finance instruction dataset containing question-answer pairs covering:
- Personal Finance
- Mutual Funds
- SIP Investing
- ETFs
- Asset Allocation
- Diversification
- Inflation
- Risk Management
- Retirement Planning
Training Summary
| Metric | Value |
|---|---|
| Base Model | TinyLlama-1.1B-Chat-v1.0 |
| Fine-Tuning Method | QLoRA |
| Quantization | 4-bit NF4 |
| Epochs | 3 |
| Final Training Loss | 2.4259 |
| Best Validation Loss | 2.409989 |
| Trainable Parameters | 0.1023% |
Trainable Parameters
Only 0.1023% of the model parameters were trained using LoRA adapters, significantly reducing memory requirements and training cost compared to full fine-tuning.
Evaluation
Validation Loss
| Epoch | Validation Loss |
|---|---|
| 1 | 2.418959 |
| 2 | 2.409989 |
| 3 | 2.412288 |
Best Validation Loss:
2.409989
Results
The model demonstrates domain adaptation toward finance-related conversations.
While the model produces more finance-focused responses in some cases, performance gains over the base TinyLlama model vary depending on question complexity and prompt format.
Further improvements may require:
- Larger finance datasets
- Additional training epochs
- More comprehensive evaluation benchmarks
- Human evaluation studies
Technical Specifications
Architecture
TinyLlama-1.1B-Chat-v1.0
Objective
Causal Language Modeling
Fine-Tuning Method
QLoRA
Adapter Format
PEFT LoRA Adapter
Model Size
- Base Model: 1.1 Billion Parameters
- Adapter Size: ~4.5 MB
- Quantization: 4-bit NF4
Future Improvements
- Larger finance datasets
- Better evaluation benchmarks
- RAG integration
- Human preference evaluation
- Hugging Face Spaces deployment
- Improved response quality and factual accuracy
Model tree for Konnnnnnnn/finance-chat-assistant
Base model
TinyLlama/TinyLlama-1.1B-Chat-v1.0