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
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:
Table with columns: Dataset, Samples| 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:
{
"messages": [
{
"role": "system",
"content": "You are a financial analyst AI assistant."
},
{
"role": "user",
"content": "Financial question"
},
{
"role": "assistant",
"content": "Financial answer"
}
]
}