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README.md

@@ -1,3 +1,95 @@
 ---
 license: apache-2.0
+language:
+- en
+base_model: microsoft/Phi-3.5-mini-instruct
+pipeline_tag: text-generation
+tags:
+- finance
+- accounts-payable
+- invoice-audit
+- fraud-detection
+- qlora
+- phi3
 ---
+
+# AP Auditor — Accounts Payable Fraud Detector
+
+Fine-tuned **Phi-3.5-mini-instruct** for Accounts Payable invoice auditing.
+Detects fraud, duplicates, pricing errors, and compliance violations instantly.
+
+![Evaluation Results](ap_auditor_evaluation.png)
+
+## Evaluation Results
+
+| Metric | Score |
+|---|---|
+| JSON Parse Success | 8/8 (100%) |
+| Action Accuracy | 7/8 (87.5%) |
+| Risk Level Accuracy | 7/8 (87.5%) |
+| Flag Detection | 5/6 (83.3%) |
+| Overall | 87.5% |
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base Model | Phi-3.5-mini-instruct |
+| Parameters | 3.8B |
+| Method | QLoRA (4-bit NF4 + double quantization) |
+| LoRA Rank | r=64, alpha=128 |
+| Training Samples | 1,219 |
+| Real Data | CORD-v2 (400 receipts) |
+| Synthetic Data | 600 AP audit scenarios |
+| Epochs | 3 |
+| Final Train Loss | 0.853 |
+| Final Val Loss | 0.137 |
+
+## Detects
+
+- `duplicate_invoice` — same invoice submitted twice
+- `unapproved_vendor` — vendor not on approved list
+- `missing_po_reference` — no PO number attached
+- `tax_discrepancy` — wrong GST rate applied
+- `round_number_fraud` — suspiciously round amounts
+- `split_invoice` — invoices split to avoid approval threshold
+- `price_mismatch` — amount exceeds contracted rate
+- `weekend_submission` — invoice submitted on weekend
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch, json, re
+
+model = AutoModelForCausalLM.from_pretrained(
+    "ratulsur/ap-auditor",
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+tok = AutoTokenizer.from_pretrained("ratulsur/ap-auditor")
+
+SYSTEM_PROMPT = """You are a senior Accounts Payable Auditor AI.
+Output ONLY a valid JSON audit result."""
+
+def audit(invoice: dict) -> dict:
+    prompt = (
+        f"<|system|>\n{SYSTEM_PROMPT}<|end|>\n"
+        f"<|user|>\nAudit this invoice:\n\n{json.dumps(invoice, indent=2)}<|end|>\n"
+        f"<|assistant|>\n"
+    )
+    pipe = pipeline("text-generation", model=model, tokenizer=tok,
+                    return_full_text=False)
+    out  = pipe(prompt, max_new_tokens=512, do_sample=False)
+    raw  = out[0]["generated_text"].strip()
+    match = re.search(r"\{.*\}", raw, re.DOTALL)
+    return json.loads(match.group()) if match else {"error": raw}
+```
+
+## Live Demo
+
+Try it: [huggingface.co/spaces/ratulsur/ap-auditor-demo](https://huggingface.co/spaces/ratulsur/ap-auditor-demo)
+
+## License
+
+Apache 2.0