FinSight AI is a specialized financial advisory assistant built by fine-tuning SmolLM2-1.7B-Instruct using QLoRA (Quantized Low-Rank Adaptation). The model has been trained on a comprehensive dataset of financial conversations to provide accurate, concise, and helpful information across various financial domains including personal finance, investing, market analysis, and financial planning.
Our evaluation demonstrates significant performance improvements across all standard NLP metrics (ROUGE-1 , ROUGE-2, ROUGE-L & BLEU), showcasing the effectiveness of our domain-specific training approach. The model exhibits enhanced capabilities with richer financial terminology usage, more precise responses, improved handling of numerical data, and greater technical accuracy - all while maintaining a compact, resource-efficient architecture suitable for deployment on consumer hardware.
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QLoRA Parameters:
- Rank (r): 64
- Alpha: 16
- Target modules: Query, Key, Value projections, MLP layers
- 4-bit NF4 quantization with double quantization
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Training Hyperparameters:
- Learning rate: 2e-4
- Epochs: 2
- Batch size: 2 (with gradient accumulation steps of 4)
- Weight decay: 0.05
- Scheduler: Cosine with restarts
- Warmup ratio: 0.15
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Hardware: Consumer-grade NVIDIA RTX 3050 GPU with 6GB VRAM
1
2@misc{FinSightAI2025,
3 author = {Zahemen, FinsightAI Team},
4 title = {FinSight AI: Enhancing Financial Domain Performance of Small Language Models Through QLoRA Fine-tuning},
5 year = {2025},
6 publisher = {GitHub},
7 journal = {GitHub repository},
8 howpublished = {\url{https://github.com/zahemen9900/FinsightAI}}
9}
