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>

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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.

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⚡ 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

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🧪 Quick Start (Copy & Run)

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))