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---
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}
}
```

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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- if strftime_now is defined %}
{%- set date_string = strftime_now("%d %b %Y") %}
{%- else %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{{- "<|eot_id|>" }}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"dtype": "bfloat16",
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 24,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": null,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"factor": 32.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_theta": 500000.0,
"rope_type": "llama3"
},
"tie_word_embeddings": true,
"transformers_version": "5.4.0",
"use_cache": true,
"vocab_size": 128256
}

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{
"bos_token_id": 128000,
"do_sample": true,
"eos_token_id": [
128001,
128008,
128009
],
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "5.4.0"
}

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{
"backend": "tokenizers",
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|eot_id|>",
"is_local": true,
"max_length": 2048,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"pad_to_multiple_of": null,
"pad_token": "<|eot_id|>",
"pad_token_type_id": 0,
"padding_side": "right",
"stride": 0,
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"
}