README.md

---
license: mit
tags:
- finance
- financial-qa
- finetuned-llm
- phi3
- phi-3-mini
- fintech
- accounting
- banking
- investment
- risk-analysis
- lora
- merged-model
language:
- en
pipeline_tag: text-generation
base_model: microsoft/phi-3-mini-4k-instruct
model_name: finance_llm_full
model_creator: devAnurag
pretty_name: Finance LLM Full
---

# 💎 Finance LLM Full — Next-Gen Financial Intelligence Model
<p align="center">
  <img src="finanace_llm.png" alt="Finance LLM Logo" width="180" style="border-radius:12px;"/>
</p>

<h1 align="center">💼 Finance LLM Full</h1>

<p align="center">
  <b>A next-generation Financial Intelligence Model<br>
  Fine-Tuned, Merged & Optimized for Real-World Finance</b>
</p>

<p align="center">
  <a href="https://huggingface.co/devAnurag/finance_llm_full">
    <img src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface" />
  </a>
  <img src="https://img.shields.io/badge/Model%20Type-Merged%20LoRA-blue" />
  <img src="https://img.shields.io/badge/Base%20Model-Phi3%20Mini%204K-green" />
  <img src="https://img.shields.io/badge/Domain-Finance%20%26%20Business-purple" />
</p>

---




**Finance LLM Full** is a high-performance, fully merged financial Large Language Model (LLM)  
designed to deliver **crystal-clear, accurate, and structured financial reasoning**.

It is trained using **LoRA fine-tuning** on top of **Phi-3 Mini 4K Instruct**, and later  
**merged into a single standalone model** for seamless deployment.

This model specializes in **Finance, Accounting, Banking, Investment, Stock Markets, and Business Analysis** —  
making it ideal for **FinTech products, AI advisors, investment copilots, and enterprise bots**.

---

# ⚡ Why Finance LLM Full is Special

### 🔹 1. Purpose-Built For Finance  
Unlike general LLMs, this model deeply understands:
- Balance Sheet Interpretation  
- Profit & Loss Breakdown  
- Cashflow Logic  
- EBITDA / EPS / ROE / DCF  
- Risk & Return Analysis  
- Banking, Loans, Limits, Credit Rules  
- Valuation Basics  
- Investment & Portfolio Concepts  

### 🔹 2. Merged Model → One File, Zero Hassle  
✔ No LoRA needed  
✔ No adapter loading  
✔ Direct plug-and-play  
✔ Works on CPU / GPU / Colab / Docker  

### 🔹 3. Small Model → Big Capability  
Powered by **Phi-3 Mini**, optimized for:
- Low latency  
- Low VRAM/RAM usage  
- Clean, structured answers  
- High domain accuracy  

---

# 🧪 Quick Start (Copy & Run)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "devAnurag/finance_llm_full"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)

prompt = "Explain the difference between EBITDA and Net Profit."
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))