Update README.md

README.md

@@ -1,8 +1,69 @@
 ---
 license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen3.5-27B
+base_model: Qwen/Qwen3.5-27B
 pipeline_tag: text-generation
----
+tags:
+  - peft
+  - lora
+  - qlora
+  - phishing
+  - email-security
+  - cybersecurity
+  - qwen
+language:
+  - en
+---
+
+# qwen35-27b-phish-qlora (QLoRA adapter)
+
+This repository contains a **QLoRA/LoRA adapter** fine-tuned on a phishing-email dataset to help classify emails as **PHISHING** or **LEGIT**.
+
+> This repo **does not** include the full base model weights. You must download the base model separately and load this adapter on top.
+
+## Base model
+- `Qwen/Qwen3.5-27B`
+
+## Dataset
+- Kaggle: `naserabdullahalam/phishing-email-dataset`
+
+## What it does
+Given an email body, the intended behavior is to output exactly one label:
+- `PHISHING`
+- `LEGIT`
+
+## Quickstart (Transformers + PEFT)
+
+### Install
+```bash
+pip install -U "transformers" "peft" "accelerate" "bitsandbytes" "torch"
+
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+BASE_ID = "Qwen/Qwen3.5-27B"
+ADAPTER_ID = "rudycaz/qwen35-27b-phish-qlora"  # this repo
+
+tok = AutoTokenizer.from_pretrained(BASE_ID, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_ID,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+)
+
+model = PeftModel.from_pretrained(model, ADAPTER_ID)
+
+email_text = """Subject: Urgent! Verify your account
+..."""
+
+prompt = (
+    "You are a security assistant. Classify the following email as PHISHING or LEGIT.\n\n"
+    f"EMAIL:\n{email_text}\n\n"
+    "Answer with exactly one word: PHISHING or LEGIT."
+)
+
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=4)
+print(tok.decode(out[0], skip_special_tokens=True))