---
language:
  - en
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
  - llama
  - text-generation
  - email
  - fraud-detection
  - phishing
  - cybersecurity
  - fine-tuned
  - lora
datasets:
  - SetFit/enron_spam
metrics:
  - accuracy
pipeline_tag: text-generation
model-index:
  - name: llama-email-fraud-detector
    results:
      - task:
          type: text-generation
          name: Email Fraud Analysis
        dataset:
          name: Enron Email + Synthetic (held-out test set)
          type: custom
        metrics:
          - name: Threat Types
            type: custom
            value: 11
---

# Llama Email Fraud Detector (bf16)

A fine-tuned **Llama-3.2-3B-Instruct** model for structured email fraud/phishing analysis. Given an email, the model outputs a detailed JSON verdict including 11 threat type labels, a 0-100 risk score, human-readable reasoning, and a suggested action.

This is the **explanation layer** of a dual-model anti-fraud pipeline. The discriminative model ([cunxin/roberta-email-fraud-detector](https://huggingface.co/cunxin/roberta-email-fraud-detector), 99.5% accuracy, <50ms) provides a fast binary pre-screen; its result is passed to this generative model as a `[CLASSIFIER HINT]` prior. The final verdict is reconciled by the backend service.

For GPUs with limited VRAM (< 12 GB), use the AWQ 4-bit quantized version: [cunxin/llama-email-fraud-detector-awq](https://huggingface.co/cunxin/llama-email-fraud-detector-awq).

---

基于 **Llama-3.2-3B-Instruct** 微调的结构化邮件欺诈/钓鱼分析模型。输入一封邮件，模型输出包含 11 种威胁类型标签、0-100 风险分数、可读推理说明和建议操作的详细 JSON 判决。

本模型是双模型反欺诈流水线的**解释层**。判别模型（[cunxin/roberta-email-fraud-detector](https://huggingface.co/cunxin/roberta-email-fraud-detector)，99.5% 准确率，<50ms）提供快速二元预筛，其结果以 `[CLASSIFIER HINT]` 先验传入本生成式模型。最终判决由后端服务融合。

低显存 GPU（< 12 GB）请使用 AWQ 4-bit 量化版：[cunxin/llama-email-fraud-detector-awq](https://huggingface.co/cunxin/llama-email-fraud-detector-awq)。

## Model Details / 模型详情

| | |
|---|---|
| **Architecture** | `LlamaForCausalLM` (Decoder-only Transformer with RoPE, GQA, SwiGLU) |
| **Base Model** | `meta-llama/Llama-3.2-3B-Instruct` |
| **Parameters** | 3,237,063,680 (3.2B) |
| **Fine-Tuning** | LoRA (r=16, alpha=32, dropout=0.1) merged into base weights |
| **Trainable Parameters** | 24,313,856 (0.75% of total) |
| **Precision** | bfloat16 |
| **Model Size** | 6.4 GB |
| **Context Window** | 4,096 tokens (inference) / 2,048 tokens (training) |
| **Vocabulary** | 128,256 tokens |

### Lineage / 模型血统

```
meta-llama/Llama-3.2-3B-Instruct (Meta AI — instruction-tuned on 3T tokens)
    │
    ├── LoRA fine-tuning (r=16, alpha=32, 7 target modules)
    │     Training: ~12K email conversations with structured JSON labels
    │     Hint injection: 75% correct / 15% adversarial / 10% no hint
    │
    ├── Merge LoRA adapters into base weights
    │
    ├─► cunxin/llama-email-fraud-detector (this model, bf16, 6.4 GB)
    │
    └─► cunxin/llama-email-fraud-detector-awq (AWQ 4-bit, 2.2 GB)
```

## Output Format / 输出格式

The model outputs structured JSON with 6 fields:

模型输出包含 6 个字段的结构化 JSON：

```json
{
  "is_fraud": true,
  "risk_score": 95,
  "confidence_level": 0.97,
  "detected_threats": ["DOMAIN_MISMATCH", "CREDENTIAL_REQUEST", "URGENCY_FEAR"],
  "reason": "The sender domain 'amaz0n-verify.com' typosquats amazon.com. The email requests account credentials via a suspicious URL and uses urgency tactics to pressure immediate action.",
  "suggestion": "Do not click any links. Do not enter any credentials. Report this email as phishing to your IT department."
}
```

### Threat Types & Scoring / 威胁类型与评分

The model detects 11 threat categories. Risk score = sum of triggered threat points (capped at 100).

模型检测 11 种威胁类别。风险分数 = 触发的威胁点数之和（上限 100）。

| Label / 标签 | Points / 分值 | Description / 描述 |
|---|---|---|
| `CREDENTIAL_REQUEST` | 35 | Asks for passwords, SSN, credit card numbers / 请求密码、身份证号、信用卡号 |
| `DOMAIN_MISMATCH` | 30 | Sender domain does not match claimed organization / 发件人域名与声称的组织不匹配 |
| `URL_DISCREPANCY` | 30 | Links point to suspicious or mismatched domains / 链接指向可疑或不匹配的域名 |
| `TOO_GOOD_TO_BE_TRUE` | 30 | Unrealistic promises (lottery wins, free money) / 不切实际的承诺（中奖、免费资金） |
| `PROMPT_INJECTION` | 30 | Attempts to manipulate AI analysis / 试图操纵 AI 分析 |
| `URGENCY_FEAR` | 15 | Pressure tactics ("act now", "account suspended") / 施压策略（"立即行动"、"账号已冻结"） |
| `REPLY_TO_MISMATCH` | 15 | Reply-To address differs from sender / 回复地址与发件人不同 |
| `GENERIC_SALUTATION` | 8 | Impersonal greeting ("Dear Customer") / 非个人化称呼（"尊敬的客户"） |
| `ANOMALOUS_TIMING` | 8 | Sent at unusual hours for the timezone / 在不寻常的时间发送 |
| `MISSING_SIGNATURE` | 8 | No professional email signature / 缺少专业邮件签名 |
| `GRAMMAR_ANOMALY` | 5 | Unusual grammar or spelling patterns / 异常的语法或拼写模式 |

**RULE D**: Any high-weight threat (>=30 pts) forces `is_fraud = true`.

**规则 D**：任何高权重威胁（>=30 分）强制 `is_fraud = true`。

## Dual-Model Pipeline / 双模型流水线

This model is designed to work with [cunxin/roberta-email-fraud-detector](https://huggingface.co/cunxin/roberta-email-fraud-detector) in a reconciled pipeline:

本模型设计为与 [cunxin/roberta-email-fraud-detector](https://huggingface.co/cunxin/roberta-email-fraud-detector) 配合使用：

```
Email Input
    │
    ├──► RoBERTa (discriminative, <50ms)
    │         │
    │         ▼
    │     is_fraud=True/False, confidence=0.97, risk_score=99
    │         │
    │         ▼  [CLASSIFIER HINT]
    ├──► Llama (generative, ~1-3s) ◄── this model
    │         │
    │         ▼
    │     Full JSON analysis (11 threat types, reasoning, suggestion)
    │
    ▼
Reconciliation (backend)
    │
    ▼
Final verdict
```

### STEP 4 Hint Rules / 提示规则

When a `[CLASSIFIER HINT]` is provided, the model applies these rules after its own independent analysis:

| Scenario / 场景 | Action / 操作 |
|---|---|
| Both agree / 两者一致 | Keep generative result / 保留生成式结果 |
| Hint=FRAUD, gen=safe, risk < 40 | Follow hint (classifier caught something subtle) / 遵循提示 |
| Hint=FRAUD, gen=safe, RULE D triggered | Override hint (generative has hard evidence) / 推翻提示 |
| Hint=NOT FRAUD, gen=fraud, RULE D triggered | Override hint (generative has hard evidence) / 推翻提示 |
| Hint=NOT FRAUD, gen=fraud, risk < 60 | Follow hint (only weak signals) / 遵循提示 |

## Usage / 使用方法

### With vLLM (Recommended) / 使用 vLLM（推荐）

```bash
# Set in .env
MODEL_PATH=cunxin/llama-email-fraud-detector

# Start service
docker compose --profile gpu up -d

# Test
curl -X POST http://localhost:8000/predict_generative \
  -H "Content-Type: application/json" \
  -d '{"sender":"security@amaz0n-verify.com","subject":"URGENT: Account locked","content":"Click to verify: http://amaz0n-secure.xyz"}'
```

### With Transformers / 使用 Transformers

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch, json

model_name = "cunxin/llama-email-fraud-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")

email = json.dumps({
    "date": "2026-02-25T10:00:00Z",
    "sender": "security@amaz0n-verify.com",
    "recipient": "you@example.com",
    "subject": "URGENT: Your account has been locked",
    "content": "Click here to verify: http://amaz0n-secure.xyz/verify"
})

messages = [
    {"role": "system", "content": "You are an email fraud analyst. Analyze the email and return a JSON verdict."},
    {"role": "user", "content": f"Analyze the following email:\n{email}"}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(input_ids, max_new_tokens=512, temperature=0.1)
response = tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```

## Training / 训练详情

### Fine-Tuning Method / 微调方法

| | |
|---|---|
| **Method** | LoRA (Low-Rank Adaptation) via `peft` + SFT via `trl.SFTTrainer` |
| **LoRA Rank** | 16 |
| **LoRA Alpha** | 32 (effective scale = 2x) |
| **LoRA Dropout** | 0.1 |
| **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
| **Optimizer** | AdamW (weight_decay=0.05, label_smoothing=0.05) |
| **LR Schedule** | Cosine with 10% linear warmup |
| **Learning Rate** | 2e-4 |
| **Epochs** | 3 |
| **Batch Size** | 2 (grad_accum=8, effective=16) |
| **Max Sequence Length** | 2,048 tokens |
| **Mixed Precision** | FP16 (CUDA) |
| **Loss** | Causal LM (next-token prediction on assistant turn only) |

### Training Data / 训练数据

~12,000 email conversations generated from the Enron corpus and synthetic sources:

约 12,000 个邮件对话，从 Enron 语料库和合成数据生成：

| Source / 来源 | Count / 数量 | Role / 用途 |
|---|---|---|
| Fraud emails (train) | ~500 per class | Primary training / 主要训练集 |
| Normal emails (train) | ~500 per class | Primary training / 主要训练集 |
| Correction pool (optional) | ~14K fraud + ~15K normal | Extra training via `--include-correction` |
| AI-generated modern emails | ~800 per class | Extra training via `--include-aigen` |

Each email is converted into a 3-turn chat conversation (system prompt -> user email -> assistant JSON verdict) with:
- **75%** include a correct `[CLASSIFIER HINT]` (teach trust of accurate priors)
- **15%** include an adversarial wrong hint (teach RULE D override)
- **10%** have no hint (teach independent reasoning)
- **60%** of fraud examples include `[HEURISTIC ANALYSIS]` context

## Hardware Requirements / 硬件要求

| Configuration / 配置 | VRAM / 显存 |
|---|---|
| bf16 (this model) | >= 12 GB (RTX 3060 desktop, RTX 4070+) |
| AWQ 4-bit ([llama-email-fraud-detector-awq](https://huggingface.co/cunxin/llama-email-fraud-detector-awq)) | >= 6 GB (RTX 3050, 3060 laptop) |

## Intended Use / 预期用途

- Email fraud/phishing detection with detailed threat analysis and human-readable reasoning / 提供详细威胁分析和可读推理的邮件欺诈/钓鱼检测
- Explanation layer for automated email security systems / 自动化邮件安全系统的解释层
- Part of a multi-model pipeline (discriminative pre-screen + generative analysis + reconciliation) / 多模型流水线的组成部分
- Microsoft Office Add-in integration for Outlook/Word / Microsoft Office 插件集成

## Limitations / 局限性

- Primarily trained on English emails; may underperform on other languages / 主要在英文邮件上训练
- Training data includes Enron corpus (early 2000s); modern attack patterns partially covered by AI-generated synthetic data / 训练数据包含 Enron 语料库，现代攻击模式部分由 AI 生成合成数据覆盖
- Inference latency ~1-3s per email (use RoBERTa for real-time filtering) / 推理延迟约 1-3 秒/封
- Structured JSON output depends on prompt engineering; edge cases may produce malformed JSON / 边缘情况可能产生格式错误的 JSON

## Related Models / 相关模型

| Model / 模型 | Type / 类型 | Size / 大小 | Speed / 速度 | Use Case / 用途 |
|---|---|---|---|---|
| [cunxin/roberta-email-fraud-detector](https://huggingface.co/cunxin/roberta-email-fraud-detector) | Discriminative | 475 MB | <50ms | Fast binary pre-screen / 快速二元预筛 |
| **cunxin/llama-email-fraud-detector** (this) | Generative | 6.4 GB | ~1-3s | Detailed threat analysis / 详细威胁分析 |
| [cunxin/llama-email-fraud-detector-awq](https://huggingface.co/cunxin/llama-email-fraud-detector-awq) | Generative (quantized) | 2.2 GB | ~1-3s | Same as above, for low VRAM / 同上，低显存版 |

## Citation / 引用

```bibtex
@misc{cunxin2025llama-email-fraud,
  title={Llama Email Fraud Detector},
  author={cunxin},
  year={2025},
  url={https://huggingface.co/cunxin/llama-email-fraud-detector}
}
```