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README.md

@@ -1,39 +1,33 @@
 ---
-base_model: mistralai/Mistral-7B-v0.1
-library_name: peft
-tags:
-- finance
-- tax
-- banking
-- investment
-- qlora
-- mistral
-- indian-finance
-- gst
 license: apache-2.0
+tags:
+  - finance
+  - tax
+  - banking
+  - investment
+  - indian-finance
+  - gst
+  - conversational
 ---
 
-# Finxan — Finance Domain LLM
+# 🏦 Finxan
 
-Fine-tuned **Mistral-7B-v0.1** using QLoRA (4-bit) on Indian finance domain examples:
-- 🧾 GST / Tax compliance & calculations
-- 🏦 B2B Invoicing & banking
-- 📈 Investment advice
+Finxan is an AI assistant specialized in Indian finance — GST, tax compliance, B2B invoicing, banking, and investments.
 
-**Training details:**
-- Hardware: Google Colab T4 GPU (free)
-- Method: QLoRA 4-bit, LoRA r=16
-- Epochs: 3
-- Dataset: 110 finance instruction examples
+## 💬 What Finxan can do
 
-## How to use
+- 🧾 Calculate GST and verify tax compliance
+- 🏦 Create and manage B2B invoices
+- 📈 Answer investment and banking queries
+- ✅ Check financial document compliance
+- 🤖 Answer questions about itself
 
+## 🚀 Quick Start
 ```python
 from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-# Load base + adapter
 base = AutoModelForCausalLM.from_pretrained(
     "mistralai/Mistral-7B-v0.1",
     torch_dtype=torch.float16,
@@ -42,16 +36,31 @@ base = AutoModelForCausalLM.from_pretrained(
 model = PeftModel.from_pretrained(base, "webdpro/finxan")
 tokenizer = AutoTokenizer.from_pretrained("webdpro/finxan")
 
-# Run inference
-prompt = """### Instruction:
-Calculate GST
+def ask_finxan(instruction, input_text=""):
+    prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{input_text}\n\n### Response:\n"
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=80,
+        temperature=0.3,
+        do_sample=True,
+        repetition_penalty=1.3,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    full = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return full.split("### Response:")[-1].strip()
 
-### Input:
-Invoice amount Rs 50000, GST rate 18%
+# Try it
+print(ask_finxan("What is your name?"))
+print(ask_finxan("Calculate GST", "Invoice Rs 50000, GST 18%"))
+print(ask_finxan("Who built you?"))
+```
 
-### Response:"""
+## 📌 Example Outputs
 
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7, do_sample=True)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
+| Question | Answer |
+|----------|--------|
+| What is your name? | I am Finxan, your AI finance assistant |
+| Calculate GST for Rs 50000 at 18% | GST: Rs 9000. Total: Rs 59000 |
+| Create a B2B invoice | Invoice created with GST applied |
+| Check GST compliance | GST valid. Compliant. |