💎 Finance LLM Full — Next-Gen Financial Intelligence Model
Finance LLM Logo
💼 Finance LLM Full
A next-generation Financial Intelligence Model
Fine-Tuned, Merged & Optimized for Real-World Finance
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
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
34model_id ="devAnurag/finance_llm_full"56tokenizer = AutoTokenizer.from_pretrained(model_id)7model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)89prompt ="Explain the difference between EBITDA and Net Profit."10inputs = tokenizer(prompt, return_tensors="pt")1112outputs = model.generate(**inputs, max_new_tokens=150)13print(tokenizer.decode(outputs[0], skip_special_tokens=True))
