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Qwen 2.5 3B QLoRA — Financial Reasoning Model

Name: Qwen-2.5-3B-QLORA
Availability: InStock

A fine-tuned version of Qwen 2.5 3B specialized in financial calculations (EBIT, EBITDA, EBITDAR) and multi-step numerical reasoning over structured inputs.

Quick Start

Install

pip install mlx-lm

Run Inference

python

from mlx_lm import load, stream_generate

model, tokenizer = load("rohan-bansode/Qwen-2.5-3B-QLORA")

prompt = """
Analyze:
Revenue = 500k
Operating Expenses = 350k
Rent = 50k

Calculate EBITDAR and explain.
"""

stop_sequences = ["<|endoftext|>", "<|im_end|>", "Human:", "Assistant:"]

print("--- Financial Sandbox Output ---")

for response in stream_generate(model, tokenizer, prompt, max_tokens=512):
    if any(stop in response.text for stop in stop_sequences):
        break
    print(response.text, end="", flush=True)

Example Output

text

EBITDAR = 200k

Explanation:
EBITDAR = Revenue - Operating Expenses + Rent
= 500k - 350k + 50k = 200k

What this model is good at

Financial reasoning (EBIT, EBITDA, EBITDAR)
Multi-step numerical calculations
Structured financial Q&A

Limitations

Not reliable for real financial decisions
Can make calculation mistakes if prompt is unclear
Limited context (~1.5k tokens)

Improvements over base model

More consistent financial calculations
Better step-by-step reasoning for numerical tasks
Reduced hallucination in structured financial prompts