This model is a LoRA adapter built on top of Qwen2.5-1.5B-Instruct and trained using QLoRA with 4-bit quantization to efficiently adapt the model for financial analysis tasks.

Model Details

Model Description

Developed by: Ahmed Elsayed Taha
Model type: Causal Language Model (LLM) LoRA Adapter
Base model: Qwen2.5-1.5B-Instruct
Fine-tuning method: QLoRA (LoRA + 4-bit Quantization)
Language(s): English
License: Apache 2.0 (inherits from base model license)
Finetuned from: Qwen/Qwen2.5-1.5B-Instruct

Model Sources

Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct

Uses

Direct Use

This model can be used for:

Financial question answering
Accounting concept explanation
Financial reasoning tasks
Understanding financial contexts
Generating structured financial responses

Downstream Use

Possible applications:

Financial analyst assistants
Document-based financial analysis systems
Financial education tools
Retrieval Augmented Generation (RAG) pipelines
Business intelligence assistants

Out-of-Scope Use

This model should not be used as:

A replacement for professional financial advisors
A source of guaranteed investment decisions
A system for high-stakes financial decisions without human verification

Training Details

Training Data

The model was fine-tuned using:

TheFinAI/Fino1_Reasoning_Path_FinQA
TheFinAI/Fino1_Reasoning_Path_FinQA_v2

Dataset statistics:

Dataset	Samples
Fino1_Reasoning_Path_FinQA	5,499
Fino1_Reasoning_Path_FinQA_v2	3,472
Total	8,971

The dataset contains:

Open-ended financial questions
Ground-truth answers
Financial reasoning paths
Generated responses

Preprocessing

The dataset was converted into Qwen instruction format:

json

{
  "messages": [
    {
      "role": "system",
      "content": "You are a financial analyst AI assistant."
    },
    {
      "role": "user",
      "content": "Financial question"
    },
    {
      "role": "assistant",
      "content": "Financial answer"
    }
  ]
}

ApexVOrteX-1/Financial-Qwen-Model

Financial Analyst AI Assistant