Qwen3 0.6B Financial Fundamentals Tool-Calling LoRA
This model is a LoRA adapter fine-tuned from Qwen/Qwen3-0.6B for financial tool calling. It is designed to convert natural-language requests about company fundamentals into structured JSON function calls.
The model does not directly answer financial questions. Instead, it maps a user request into a valid call to a fundamentals retrieval function, including companies, requested metrics, and year ranges.
Intended Use
This model is intended for use inside an agentic financial assistant or tool-routing system. A future orchestrator agent can call this model when a user asks for financial fundamentals data.
Example user request:
Show the revenue and free cash flow for Apple and Microsoft from 2018 to 2023.
Expected model output:
{
"action": "call",
"function": "get_fundamentals",
"arguments": {
"queries": [
{
"symbols": ["Apple", "Microsoft"],
"metrics": ["Revenue", "Free Cash Flow"],
"start_year": 2018,
"end_year": 2023
}
]
}
}
Training
The adapter was trained with LoRA on a synthetic financial tool-calling dataset. The dataset contains user-style financial requests paired with deterministic JSON completions. The training objective is to produce valid JSON tool calls while preserving:
- Company names
- Requested financial metrics
- Start and end years
- Company-specific metric assignments
Limitations
This model is an initial research/prototype model. It should not be used as a financial advisor and does not retrieve live financial data by itself. The generated JSON should be validated before execution by downstream tools.