💎 Finance LLM Full — Next-Gen Financial Intelligence Model
💼 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)
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))
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