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README.md

@@ -0,0 +1,339 @@
+---
+license: apache-2.0
+language:
+- zh
+- en
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+tags:
+- cybersecurity
+- security
+- network-security
+---
+# 🌐 SecGPT：全球首个网络安全开源大模型
+
+## 🔍 模型简介
+
+**SecGPT** 是由 **云起无垠** 于 2023 年正式推出的开源大模型，专为网络安全场景打造，旨在以人工智能技术全面提升安全防护效率与效果。
+
+> ✅ 我们的愿景：推动网络安全智能化，为社会构建更安全的数字空间
+>  🚀 我们的使命：让每一家企业，都能拥有一个“懂安全”的智能助手
+
+SecGPT 融合了自然语言理解、代码生成、安全知识推理等核心能力，已成功落地多个关键安全任务场景：
+
+- **🛠 漏洞分析**：理解漏洞成因、评估影响范围、生成修复建议
+- **🧭 日志与流量溯源**：还原攻击路径、分析攻击链，辅助事件复盘
+- **⚠️ 异常检测**：识别潜在威胁，提升安全感知与响应能力
+- **🎯 攻防推理**：服务于红队演练、蓝队分析，支撑实战决策
+- **📜 命令解析**：分析攻击脚本，识别意图与高危操作
+- **💬 安全知识问答**：团队“即问即答”的知识引擎
+
+
+
+## 🔧 最新动态
+
+- **2025年4月**：SecGPT V2.0 发布，全面升级安全理解与任务执行能力
+- **2024年3月**：轻量化版本 SecGPT-Mini 正式开源，可在 CPU 上高效运行
+- **2023年12月**：SecGPT 正式发布，成为全球首个开源网络安全大模型
+
+
+
+## 📂 开源资源
+
+- ##### 模型源码与文档：
+
+  -  https://github.com/Clouditera/secgpt
+
+- **数据集下载地址：**
+  - https://huggingface.co/datasets/clouditera/security-paper-datasets
+
+
+
+
+## 🔧 模型部署
+
+SecGPT 支持通过 vLLM 高性能推理框架部署，适用于 **低延迟、高并发、大吞吐量** 的安全模型服务场景。
+
+环境准备与服务启动：
+
+```shell
+# 创建 Python 环境（建议 Python 3.10+）
+conda create -n secgpt-vllm python=3.10 -y
+conda activate secgpt-vllm
+
+# 安装 vLLM（需具备 PyTorch + CUDA）
+pip install --upgrade pip
+pip install vllm
+
+
+# 启动server 服务
+CUDA_VISIBLE_DEVICES= xxx(GPU index) \
+vllm serve ./secgpt \
+  --tokenizer ./secgpt \
+  --tensor-parallel-size 4 \
+  --max-model-len 32768 \
+  --gpu-memory-utilization 0.9 \
+  --dtype bfloat16 \
+  
+# 示例请求
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "secgpt",
+    "messages": [{"role": "user", "content": "什么是 XSS 攻击？"}],
+    "temperature": 0.7
+  }'
+```
+
+
+
+
+
+## 本轮更新亮点：
+
+### 1. 更强的基座能力：通用+安全深度融合
+
+我们基于 **Qwen2.5-Instruct 系列** 与 **DeepSeek-R1 系列** 模型，结合自建安全任务集与安全知识库， 在 **8台A100 GPU** 集群上持续训练一周以上，完成大规模预训练 + 指令微调 + 强化学习，  显著提升模型在安全场景中的**理解、推理与响应能力**。
+
+下图展示了一次训练过程中各关键指标的演化轨迹：
+
+- **训练与验证损失（train/loss 与 eval/loss）**：二者均呈现出平稳下降趋势，说明模型在训练集与验证集上均持续收敛，未出现过拟合迹象。
+- **学习率曲线（train/learning_rate）**：采用典型的 Warmup + 衰减策略，有效提升了早期训练的稳定性与收敛速度。
+- **梯度范数（train/grad_norm）**：整体波动平稳，仅在少数步数存在轻微尖峰，未出现梯度爆炸或消失，表明训练过程健康稳定。
+- **评估表现**：`eval/runtime` 与 `eval/samples_per_second` 波动范围小，说明在评估过程中系统资源使用高效，推理吞吐量稳定。
+- **其他指标**如训练轮数（train/epoch）、输入 token 数量（train/num_input_tokens_seen）等也表明训练过程如期进行，达成预期计划。
+
+![730deaf7-f202-4dd3-99e1-f659106f02fa](./images/730deaf7-f202-4dd3-99e1-f659106f02fa.png)
+
+### 2. 更大的高质量安全语料库：私有 + 公共数据双轮驱动
+
+我们已构建了一个超大规模、结构完备的网络安全语料库，总量超过 **5TB**、共计 **106,721 个原始文件**，其中超过 **40% 内容为人工精选与结构化处理**。私有数据部分系统整合了具备 **70+ 字段 / 14 类结构标签体系** 的安全数据资源，经过统一清洗、语义标注与重构，构建出 **数百亿 Tokens 级**的高质量语料，为大模型深度推理能力提供坚实支撑。
+
+下图展示了该语料库的构成维度，整体采集逻辑遵循“**理论支撑 — 实战对抗 — 应用落地**”三层结构体系：
+
+- **理论支撑**：涵盖法律法规、学术论文、行业报告等权威资料，为模型提供稳固的知识基座；
+- **实战对抗**：包括漏洞详情、CTF题库、日志流量、恶意样本与逆向分析等数据，提升模型对真实攻击行为的识别与追踪能力；
+- **应用落地**：涵盖安全社区博客、教育培训资料、安全知识图谱与自动化策略，增强模型在安全运营、辅助决策等场景中的适配能力。
+
+![c93a1385-6c8b-4e88-80b8-965ca366b8d6](./images/c93a1385-6c8b-4e88-80b8-965ca366b8d6.png)
+
+
+
+## 🧪 模型评测与能力分析
+
+### 1. 模型能力评测：全面指标跃升，实战智能初现
+
+为全面评估 SecGPT 的安全实战能力，我们构建了一套覆盖**安全证书问答、安全通识、编程能力、知识理解与推理能力**的综合评估体系，主要采用以下标准化数据集：CISSP、CS-EVAL、CEVAL、GSM8K、BBH。
+
+| **评测集名称** | **简要说明**                                                 | **参考链接**                                                 |
+| -------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| **CISSP**      | 权威信息安全认证体系，考察模型在安全管理、访问控制、风险治理等领域的专业知识覆盖度与答题准确率，适用于评估模型在通用信息安全领域的掌握程度。 | [ISC² 官方网站](https://www.isc2.org/Certifications/CISSP) [认证考试指南](https://web.lib.xjtu.edu.cn/info/1117/6461.htm) |
+| **CS-Eval**    | 面向网络安全任务的大模型能力综合评测集，覆盖 11 个网络安全主类、42 个子类，共计 4369 道题目，包含选择题、判断题、知识抽取等题型，兼顾知识性与实战性，用于评估模型的安全通识与任务执行能力。 | [ModelScope 数据集](https://modelscope.cn/datasets/cseval/cs-eval/)  [论文介绍 (arXiv)](https://arxiv.org/abs/2411.16239) |
+| **C-Eval**     | 中文能力评估。由上海交通大学、清华大学和爱丁堡大学的研究人员在2023年5月联合推出。包含13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 | [论文链接 (arXiv)](https://arxiv.org/abs/2305.08322)  [CSDN 实践介绍](https://blog.csdn.net/2401_85343303/article/details/139698577) |
+| **GSM8K**      | 解决数学问题的能力。Google开发的一个数学问题求解数据集，包含大约8,000个高中到大学水平的数学问题。 | [GSM8K ](https://github.com/openai/grade-school-math) [GitHub](https://github.com/openai/grade-school-math) |
+| **BBH**        | 复杂语言理解能力。由Google、斯坦福等研究人员开发的数据集，包含大量复杂语言理解任务的集合，可能包含需要深度推理、常识运用或复杂认知技能的任务。 | [论文链接 (arXiv)](https://arxiv.org/abs/2206.04615)         |
+
+在与原始模型 SecGPT-mini 的对比中，训练后的模型在所有指标上均实现大幅跃升，具体如下：
+
+#### 1.1 模型纵向评测对比
+
+| **模型版本**    | **CISSP**    | **CS-EVAL**   | **CEVAL**    | **GSM8K**    | **BBH**      |
+| --------------- | ------------ | ------------- | ------------ | ------------ | ------------ |
+| **SecGPT-mini** | 25.67        | 39.64         | 37.50        | 3.87         | 21.80        |
+| **SecGPT-1.5B** | 71.09🔺+45.42 | 81.53 🔺+41.89 | 53.5 🔺+16.00 | 57.47🔺+53.60 | 45.17🔺+23.37 |
+| **SecGPT-7B**   | 78.23🔺+52.97 | 85.12 🔺+45.48 | 72.89🔺+35.39 | 76.88🔺+73.01 | 67.08🔺+45.28 |
+| **SecGPT-14B**  | 77.37🔺+51.70 | 86.12 🔺+46.48 | 59.45🔺+29.95 | 88.25🔺+84.38 | 75.90🔺+54.10 |
+
+📈 **能力跃升解读：**
+
+- **mini → 1.5B**：具备“能答对”的基础问答能力，适配中低复杂度任务；
+- **1.5B → 7B**：推理深度、泛化能力显著增强，能理解任务意图并构建较为完整的解决路径；
+- **7B → 14B**：能力跃迁至“类专家”级，能够处理高复杂度推理、安全策略制定等高阶任务。
+
+#### 1.2 模型横向评测对比
+
+相较于基础模型 Qwen2.5-Instruct，SecGPT 在所有评测指标上均实现实质性超越，反映出我们在数据构建、微调范式、安全任务精调机制上的整体优化成效：
+
+| 模型版本         | **CISSP**  ↑ | **CS-EVAL  ↑** | **CEVAL ↑** | **GSM8K ↑** | **BBH ↑** |
+| ---------------- | ------------ | -------------- | ----------- | ----------- | --------- |
+| **Qwen2.5-1.5B** | 52.97        | 71.66          | 59.91       | 61.03       | 43.44     |
+| **SecGPT-1.5B**  | 71.09        | 81.53          | 53.5        | 57.47       | 45.17     |
+| **Qwen2.5-7B**   | 66.30        | 84.66          | 74.97       | 80.36       | 71.20     |
+| **SecGPT-7B**    | 78.23        | 85.12          | 72.89       | 76.88       | 67.08     |
+| **Qwen2.5-14B**  | 71.09        | 86.22          | 68.57       | 90.03       | 78.25     |
+| **SecGPT-14B**   | 77.37        | 86.12          | 59.45       | 88.25       | 75.90     |
+
+💡 **洞察亮点：**
+
+- 在 CISSP 和 CS-EVAL 等安全类数据集上，SecGPT 在所有参数规模下均表现优于 Qwen2.5 同规格版本；
+- 表明我们构建的安全任务指令集与精调策略已显著提升模型的实��应用能力与专业问答深度。
+
+### 2. 安全能力提升：更全、更准、更专业
+
+本轮升级中，SecGPT 在安全知识问答方面完成了从**信息整合**到**逻辑输出**的能力跃迁，具体体现在：
+
+- **知识覆盖更全面**：引入了涵盖法律法规、攻击战术、逆向分析等14类安全知识领域的结构化语料；
+- **答案生成更精准**：通过多轮对话控制与语义优化技术，提升了问答对齐率与上下文记忆稳定性；
+- **推理能力更突出**：具备多段知识联结与复合逻辑推演能力，能完成如攻击链分析、威胁研判等复杂任务。
+
+#### 2.1 渗透测试场景能力：
+
+SecGPT 能够模拟渗透攻击流程，从信息收集、漏洞利用到提权横向，具备关键工具命令分析、Payload 构造、利用链生成等能力。
+
+![img](./images/st1.png)
+
+![img](./images/st2.png)
+
+![img](./images/st3.png)
+
+![img](./images/st4.png)
+
+#### 2.2  日志分析和流量分析能力
+
+在安全日志与网络流量场景下，SecGPT 能自动识别异常事件、构建攻击链图谱、抽取关键 IOC（Indicator of Compromise），辅助完成事件溯源与告警分类。
+
+![img](./images/log1.png)
+
+![img](./images/log2.png)
+
+#### 2.3 逆向分析能力
+
+基于对反汇编、API 调用序列、加壳行为等低层数据的理解，SecGPT 能辅助完成恶意样本的静态分析、特征提取与家族归类，具备一定的逆向辅助解读能力。
+
+![img](./images/nx1.png)
+
+![img](./images/nx2.png)
+
+![img](./images/nx3.png)
+
+#### 2.4 代码审计能力
+
+```typescript
+import java.util.*;
+import java.util.stream.Collectors;
+
+public class AverageCalculator {
+
+    public static double calculateAverage(Object input, boolean strict) {
+        if (strict) {
+            System.out.println("Running in STRICT mode");
+
+           
+            @SuppressWarnings("unchecked")
+            List<Number> numbers = (List<Number>) input;
+            double total = 0;
+            for (Number num : numbers) {
+                total += num.doubleValue();
+            }
+            return total / numbers.size();
+        } else {
+            System.out.println("Running in SAFE mode");
+
+            List<Double> parsed = parseInputSafe(input);
+            double total = parsed.stream().mapToDouble(Double::doubleValue).sum();
+            return total / parsed.size();
+        }
+    }
+
+   
+    public static List<Double> parseInputSafe(Object input) {
+        List<Double> result = new ArrayList<>();
+
+        if (input instanceof String) {
+            String s = (String) input;
+            if (!s.matches("\\d+")) {
+                throw new IllegalArgumentException("String must contain only digits.");
+            }
+            for (char c : s.toCharArray()) {
+                result.add(Double.parseDouble(String.valueOf(c)));
+            }
+        } else if (input instanceof List<?>) {
+            for (Object obj : (List<?>) input) {
+                try {
+                    result.add(Double.parseDouble(obj.toString()));
+                } catch (NumberFormatException e) {
+                    throw new IllegalArgumentException("Invalid element in list: " + obj);
+                }
+            }
+        } else {
+            throw new IllegalArgumentException("Unsupported input type: " + input.getClass());
+        }
+
+        return result;
+    }
+
+   
+    public static void main(String[] args) {
+        List<TestCase> testCases = Arrays.asList(
+            new TestCase("12345", false),
+            new TestCase("12345", true),           
+            new TestCase(Arrays.asList(1, 2, "3", "4"), false),
+            new TestCase(Arrays.asList(1, 2, 3, "four"), false),
+            new TestCase(Arrays.asList(1, 2, 3, "four"), true)   
+        );
+
+        for (int i = 0; i < testCases.size(); i++) {
+            TestCase tc = testCases.get(i);
+            System.out.println("\n--- Test Case " + (i + 1) + " | strict=" + tc.strict + " ---");
+            try {
+                double avg = calculateAverage(tc.input, tc.strict);
+                System.out.println("✅ Average: " + avg);
+            } catch (Exception e) {
+                System.out.println("❌ Error: " + e.getMessage());
+            }
+        }
+    }
+
+   
+    static class TestCase {
+        Object input;
+        boolean strict;
+
+        TestCase(Object input, boolean strict) {
+            this.input = input;
+            this.strict = strict;
+        }
+    }
+}
+```
+
+![img](./images/dmsj.png)
+
+#### 2.5 工具使用
+
+![img](./images/gjsy.png)
+
+![img](./images/gjsy1.png)
+
+
+
+
+
+
+
+## 📮 联系我们
+<div align=center><img src="images/20240220-164216.jpg" alt="" width="40%" height="40%">
+</div>
+SecGPT 是一个面向网络安全领域的大模型开源项目，我们相信开放、协作、共享的力量，能够不断推动行业智能化进程。
+
+我们诚挚邀请全球的安全研究者、工程师、爱好者共同参与 SecGPT 的建设与优化：
+
+- 🧠 提出使用建议或功能需求
+- 🐞 反馈问题并提交 Issue
+- 💻 贡献代码与安全任务数据集
+- 📢 参与使用经验交流与最佳实践分享
+
+📬 若您对模型有任何疑问或合作意向，欢迎通过 GitHub 参与贡献或联系云起无垠团队。我们期待与更多志同道合的伙伴一起，共建“真正懂安全”的 AI 系统。
+
+## ⭐ Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=Clouditera/secgpt&type=Date)](https://star-history.com/#Clouditera/secgpt&Date)
+
+## ⚠️ 免责声明
+
+在使用 SecGPT 时，请您注意以下事项：
+
+- 本项目为研究与交流目的所构建，输出内容可能受限于模型训练数据的覆盖范围；
+- 用户在使用模型过程中，应自行判断其输出的正确性与适用性；
+- 若您计划将本模型用于 **公开发布或商业化部署**，请务必明确承担相关法律和合规责任；
+- 本项目的开发者对因使用本模型（包括但不限于模型本身、训练数据、输出内容）所可能产生的任何直接或间接损害概不负责。

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+}

tokenization_baichuan.py

@@ -0,0 +1,232 @@
+# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
+
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {},
+    "tokenizer_file": {},
+}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
+
+
+class BaichuanTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token=None,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            sp_model_kwargs=self.sp_model_kwargs,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.vocab_file)
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for i, token in enumerate(tokens):
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special and i != 0:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
+        if token_ids_1 is None:
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+        return (
+            bos_token_id
+            + ([0] * len(token_ids_0))
+            + eos_token_id
+            + bos_token_id
+            + ([0] * len(token_ids_1))
+            + eos_token_id
+        )
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+        sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
+
+        if token_ids_1 is not None:
+            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
+
+        return output
+

tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}