Update README.md

README.md

@@ -29,14 +29,16 @@ tags:
 - text-generation
 ---
 
-# 🛡 SRA-RiskGate-4B
+# 🛡️ SRA-RiskGate-4B
 
+[![PyPI Version](https://img.shields.io/pypi/v/sra-riskgate?style=for-the-badge&logo=pypi&color=blue)](https://pypi.org/project/sra-riskgate/)
+[![PyPI Downloads](https://img.shields.io/pypi/dm/sra-riskgate?style=for-the-badge&logo=pypi&color=orange)](https://pypi.org/project/sra-riskgate/)
 [![Interactive Demo](https://img.shields.io/badge/Demo-Interactive%20Space-blue?style=for-the-badge&logo=huggingface)](https://sriram1983007-sra-riskgate-demo.static.hf.space/index.html)
 [![Dataset](https://img.shields.io/badge/Dataset-Risk%20Bench-green?style=for-the-badge&logo=huggingface)](https://huggingface.co/datasets/sriram1983007/sra-stablecoin-risk-bench)
 [![GGUF Quantized](https://img.shields.io/badge/GGUF-Q4__K__M-yellow?style=for-the-badge&logo=huggingface)](https://huggingface.co/sriram1983007/SRA-RiskGate-4B-Q4_K_M-GGUF)
 [![Ollama Registry](https://img.shields.io/badge/Ollama-Registry-black?style=for-the-badge&logo=ollama)](https://ollama.com/sriram1983007/sra-riskgate)
 
-**SRA-RiskGate-4B** is an autonomous risk scoring, compliance verification, and dispute adjudication model fine-tuned on top of `Qwen/Qwen3-4B-Instruct-2507`.
+**SRA-RiskGate-4B** is an autonomous risk scoring, compliance verification, and dispute adjudication engine fine-tuned on top of `Qwen/Qwen3-4B-Instruct-2507`.
 
 A specialized 4B parameter model engineered for both ends of a stablecoin payment's operational lifecycle:
 - **Pre-Settlement Risk Gate:** Ingests payment requests (x402 requests, EIP-3009 authorizations, or standard ERC-20 transfers), policy constraints, and deterministic verification tool outputs (sanctions hits, attestation checks, signature status) to return structured `approve` / `hold` / `reject` decisions with explicit rationales.
@@ -44,9 +46,103 @@ A specialized 4B parameter model engineered for both ends of a stablecoin paymen
 
 ---
 
-### 🦙 Quickstart with Ollama
+## 📦 Quickstart via Python SDK (PyPI)
+
+Install the official Python client directly:
+
+```bash
+pip install sra-riskgate
+
+```
+
+Run an automated transaction audit:
+
+```python
+from sra_riskgate import RiskGate, TransactionPayload
+
+gate = RiskGate()
+
+tx = TransactionPayload(
+    chain_id=1,
+    token="USDC",
+    sender="0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045",
+    recipient="0x1111111111111111111111111111111111111111",
+    amount=5000.0,
+    peg_deviation_pct=0.02
+)
+
+verdict = gate.inspect(tx)
+print("Decision:   ", verdict.decision.value)
+print("Risk Score: ", verdict.risk_score)
+print("Reasoning:  ", verdict.reasoning)
+print("Remedy:     ", verdict.recommended_remedy)
+
+```
+
+---
+
+## 🦙 Quickstart with Ollama
 
 Run locally in one line via the official Ollama registry:
 
 ```bash
-ollama run sriram1983007/sra-riskgate
+ollama run sriram1983007/sra-riskgate
+
+```
+
+---
+
+## 🚀 Transformers Quickstart
+
+```python
+import json
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_ID = "sriram1983007/SRA-RiskGate-4B"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+SYSTEM_PROMPT = """You are SRA-RiskGate-4B, an autonomous stablecoin compliance and risk adjudication model.
+Analyze the transaction payload and return a strictly formatted JSON output with keys:
+"decision", "risk_score", "categories_detected", "reasoning", "recommended_remedy"."""
+
+tx_context = {
+    "chain_id": 1,
+    "token": "USDC",
+    "sender": "0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045",
+    "recipient": "0x1111111111111111111111111111111111111111",
+    "amount": 5000.0,
+    "peg_deviation_pct": "0.02%"
+}
+
+prompt = (
+    f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
+    f"<|im_start|>user\nInspect transaction:\n{json.dumps(tx_context, indent=2)}<|im_end|>\n"
+    f"<|im_start|>assistant\n"
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1, do_sample=False)
+result = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print(result)
+
+```
+
+---
+
+## 📊 Benchmark Dataset
+
+The model is evaluated and fine-tuned against **`sriram1983007/sra-stablecoin-risk-bench`**, a benchmark spanning pre-settlement authorization risks, off-peg cascades, multi-chain gas exploits, flash loan volume spikes, and smart contract escrow dispute resolutions.
+
+---
+
+## ⚖️ License
+
+Distributed under the Apache 2.0 License.