ZenFinance-3B is a highly specialized, agentic large language model designed for personal finance applications. Fine-tuned from Llama-3.2-3B-Instruct, this model acts as both a financial advisor and a UI agent.

It is trained to "think" before it speaks using <thought> tags, and can execute frontend actions (like adding expenses or setting savings goals) by outputting strict JSON inside <tool_call> tags.

⚡ Model Highlights

Architecture: 3B Parameters (Llama-3.2 base)
Format: GGUF (q4_k_m - highly compressed, runs on <3GB RAM)
Capabilities: Financial reasoning, budgeting advice, and structured JSON tool calling.
Training: Fine-tuned using QLoRA via Unsloth on a mixed dataset of 4,000 financial and agentic interactions.

🛠️ How it Works (Prompting & Output)

To get the model to trigger actions, you must use the standard Llama-3 chat template and include the system prompt defining its tools.

System Prompt:

"You are ZenFinance AI, a minimalist personal finance assistant. You provide calm, objective financial advice and can execute actions using tools."

Example Interaction

User:

"I just spent $12 on lunch."

ZenFinance-3B Output:

text

<thought>
User spent $12 on lunch. Category: Food. This is an expense.
I will trigger the add_transaction tool to update their dashboard.
</thought>
<tool_call>
{"action": "add_transaction", "amount": 12, "category": "Food", "type": "expense"}
</tool_call>
I've added that $12 food expense to your dashboard.

3amthoughts/zenfinance-3b

🧘‍♂️ ZenFinance-3B-Agent (GGUF)

⚡ Model Highlights

🛠️ How it Works (Prompting & Output)

Example Interaction

3amthoughts/
zenfinance-3b