初始化项目，由ModelHub XC社区提供模型

Model: cunxin/llama-email-fraud-detector Source: Original Platform
2026-07-02 23:56:17 +08:00
commit e307a93057
8 changed files with 493 additions and 0 deletions
--- a/.gitattributes
+++ b/.gitattributes
@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
--- a/README.md
+++ b/README.md
@@ -0,0 +1,284 @@
+---
+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}
+}
+```
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,93 @@
+{{- 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 %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,40 @@
+{
+  "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
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,12 @@
+{
+  "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"
+}
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:919b60928fd0c83bb839cb086ab0c2d8eb9a821fe3dd343b90ac1e54357a9f15
+size 6425529112
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:912e72517f7521f3273166a7879fe54c52b397f8c48ea80f8f1ed794beb24c09
+size 17210184
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,22 @@
+{
+  "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"
+}