README.md

README.md

@@ -1,73 +1,72 @@
-# 🧠 Finance LLM — Fine-Tuned Phi-3 Mini (LoRA + Full Merge)
-
-This model is a **financial question-answering LLM**, fine-tuned on a curated dataset using:
-
-- **Base Model:** microsoft/phi-3-mini-4k-instruct  
-- **Training Method:** LoRA (Rank=16, Alpha=32, Dropout=0.05)  
-- **Merged Model:** Yes (LoRA + Base fully merged into one model)
-
 ---
-
-## 🚀 Capabilities
-
-- ✓ Financial Q&A  
-- ✓ Balance sheet understanding  
-- ✓ Profit/Loss interpretation  
-- ✓ Ratios / EBITDA / EPS explanation  
-- ✓ Market news reasoning  
-- ✓ Risk assessment  
-- ✓ Investment style suggestions  
-- ✓ Banking & loan related Q&A  
-
-This model is optimized for **Indian finance use cases**.
-
+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
 ---
 
-## 📊 Training Details
+# 💎 Finance LLM Full — Next-Gen Financial Intelligence Model
 
-| Setting | Value |
-|--------|-------|
-| Epochs | 2 |
-| LoRA Rank | 16 |
-| Batch Size | 2 |
-| Learning Rate | 2e-4 |
-| Dataset | FinanceQA (custom cleaned) |
-| Hardware | A100 / T4 / Colab GPU |
+**Finance LLM Full** is a high-performance, fully merged financial Large Language Model (LLM)  
+designed to deliver **crystal-clear, accurate, and structured financial reasoning**.
 
-Purpose:  
-To create a small lightweight but finance-aware LLM for **startups, fintech apps, compliance tools, and internal company use**.
+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.
 
----
-
-## 📁 Dataset
-
-This model was trained on:
-
-- Financial questions & answers  
-- Corporate filings  
-- Stock-market explanations  
-- Real-world financial reasoning examples  
-
-Dataset used: **AfterQuery/FinanceQA** (public)
+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**.
 
 ---
 
-## 🧩 Model Architecture
-
-Based on Phi-3 mini (3.8B parameters) with LoRA applied to:
-
-- Self-attention QKV projections  
-- Output projections  
-- MLP up/down projections  
-
-Merged model = pure FP16 weights → **No LoRA needed at inference**.
+# ⚡ 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  
 
 ---
 
-## 💡 Usage Example
+# 🧪 Quick Start (Copy & Run)
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
 model_id = "devAnurag/finance_llm_full"
@@ -75,7 +74,7 @@ model_id = "devAnurag/finance_llm_full"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
 
-prompt = "Explain EBITDA in simple terms."
+prompt = "Explain the difference between EBITDA and Net Profit."
 inputs = tokenizer(prompt, return_tensors="pt")
 
 outputs = model.generate(**inputs, max_new_tokens=150)