Llama-3-8B Fine-Tuned for Credit Analysis
This model, developed by Dushyant4342, is a specialized fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct (8 billion parameters, 8192 tokens context length) designed to function as an expert credit analyst. Its primary capability is to analyze structured credit report data, specifically 'before' and 'after' snapshots, and generate concise, balanced summaries of the most significant positive and negative changes.
Key Capabilities
- Automated Credit Report Summarization: Translates complex, multi-account credit data into an easy-to-understand narrative.
- Change Analysis: Identifies and summarizes key positive and negative changes between two consecutive credit reports.
- Structured Input Processing: Requires a specific prompt structure including a system prompt defining its expert role and a user prompt with structured credit data.
Training Details
The model was fine-tuned on a proprietary dataset of approximately 10,000 anonymized customer credit profiles, each containing 'before' and 'after' credit information. Low-Rank Adaptation (LoRA) was utilized for efficient fine-tuning, adding only about 0.4% trainable parameters. Training was conducted for 1 epoch on an A100 GPU with a max sequence length of 4096.
Limitations and Out-of-Scope Use
This model's knowledge is limited to its training data and does not possess real-time financial information. It should not be used for:
- Making final, automated credit approval or rejection decisions without human oversight.
- Providing financial advice to consumers.
- Any application violating data privacy or financial regulations.
It serves as an analytical assistant, not a definitive decision-making tool, and may reflect biases from its training dataset.