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Model: sakmist/Tifa-DeepsexV2-7b-F16 Source: Original Platform
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---
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base_model:
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- deepseek-ai/deepseek-r1-14b
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language:
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- zh
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- en
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library_name: transformers
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tags:
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- incremental-pretraining
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- sft
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- reinforcement-learning
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- roleplay
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- cot
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license: apache-2.0
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---
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# Tifa-DeepSexV2-7b-MGRPO
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本文是针对原官方发布的gguf改变成safetensors（F16版本）
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- **原始模型**：[Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
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- **GGUF**: [F16](https://huggingface.co/ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-F16) | [Q8](https://huggingface.co/ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q8) | [Q4](https://huggingface.co/ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4) （Q4损失较大，推荐Q8以上）
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- **Demo APK**: [点击下载](http://app.visionsic.com/download/projectchat.apk)
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- **简单的前端**：[Github链接](https://github.com/Value99/Tifa-Deepsex-OllamaWebUI)
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- **必看教程**：[BiliBili视频教程](https://space.bilibili.com/483051186)
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- **safetensors转换作者**[Github链接](https://github.com/nkggya)
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本模型基于Qwen2.5 7b进行深度优化，具有100万字上下文能力，借助Tifa_220B生成的数据集与创新型的MGRPO算法，提供卓越的角色扮演体验。**本模型未特殊优化违规内容生成，仅在减少拒绝上努力。**
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非常抱歉列位，我延期的原因是严重低估了MGRPO的训练效率。实测发现，经过我“优化”的框架虽然能提升性能，但是训练效率下降3倍之多，显存容量占用也是三倍之多。所以延期至今，目前训练已完成7%，但是效果完全超越14b、甚至32b模型。已足够证明策略优越，遂上传模型。目前正在从服务器中回传。很快将会上载。训练还在继续，将和之前一样分为验证版、Chat版和Crazy版本。目前为验证版。
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In the term "deepsex," the Chinese character "sex" (性) refers to:
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- The "human nature" in philosophical context (人性的性)
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- The "character" in personal disposition (性格的性)
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- The "individuality" in unique traits (个性的性)
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- The "innate essence" in the Confucian concept of "human nature is inherently good" (性本善的性)
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Please note this "sex" should not be interpreted as sexual relations, but rather as the multidimensional Chinese philosophical concept of "Xing" (性).
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## 鸣谢
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- **上海左北科技提供算法与算力和语料**[企业网址](https://leftnorth.com/)
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- **Deepseek团队共享GRPO算法**
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- **Qwen团队提供优秀开源底座**
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- **母校上海复旦大学**
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- **PRIME团队提供优化思路**
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## 版本介绍：
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- **Tifa-DeepSexV2-7b**
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- 验证模型，测试MGRPO算法有效性、优越性、研究潜空间循环性能提升有效性。为训练进度7%的模型。
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- **Tifa-DeepSexV2-7b-Chat**
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- 训练中...
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- **Tifa-DeepSexV2-7b-Crazy**
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- 训练中...
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## 训练效果
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## 实现
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🔥 **经过训练后**：
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1. **具备思维链**：逻辑能力，关联能力提升
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2. **自发思考**：思维链在训练中自发生成，提供最优解决思路
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3. **特定词汇增加**：进行“具有深度”的角色扮演对话时，显著增加了相关词汇量，解决原始权重预训练数据不足问题
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4. **更少拒绝**：减少了拒绝现象，但因为是企业训练，安全性还是稍作保留
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5. **文学性能提升**：强化学习中更多的提升了文学性，使其输出更具有小说感觉
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## 模型亮点
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🔥 **四阶段进化架构**：
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1. **增量预训练**：注入0.1T Token 小说，增强文本连贯性，理解更多场景
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2. **Tifa-COT-SFT冷启动**：使模型学会思考策略，提升逻辑性能与上下文关联
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3. **MGROP**：改进GRPO算法，解决GRPO无法奖励角色扮演数据问题，引入多次奖励，提升模型效果
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4. **防重复DPO**：使用DPO防止模型复读、增强政治安全性。
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💡 **工程创新**：
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- 改进GRPO算法，使其可以用来训练文学类内容
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- 改进反馈策略，前置向量确认法提高模型训练性能
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- 改进训练时Transformers传播路径，激发模型深层次潜能
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## MGRPO与层传播：
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- **算法改变**：原始GRPO仅通过ORM策略进行偏好学习，但无法评判文学内容生成质量，本次训练使用**上海交通大学**博士**魏XX**、**洪XX**的启发，使用逻辑学算法，解决文学ORM策略设计难点，并且二次返回修改提示词进行错误总结。每次调整均经历2次完整传播过程，为GRPO+GRPO，所以暂时命名为MGRPO。
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- **构架改变**：尝试改变Transformers传播方式，在层内循环处理进行训练，受到Universal Transformers与最新潜空间启发，在训练中让部分层循环激活，为了避免梯度爆炸使用梯度裁切技术，测试发现模型性能得到提升，更多工作还在测试中。
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## 奖励函数设计：
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- **为了让模型输出效果更好，我联合左北科技语言学小组，使用上交大博士逻辑学思路设计多个主要函数**
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- 逻辑奖励函数：把文本向量化之后，通过逻辑学算法，提取角色向量空间中的分布，与对话中倾向是否一致。
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- 文笔奖励函数：采用文笔库，选取10000条著作中截取的优秀文字片段作为比较，计算在空间中的离散分布，看能否匹配。
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- 格式奖励函数：最简单的函数，包括重复性惩罚、输出内容换行惩罚、过拟合词惩罚、输出格式与system差距惩罚。
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- 连贯性奖励函数：使用左北科技训练的向量空间计算器（我也不知道什么玩意）判断文本中断层的残差来返回函数。
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- （0216新增）人体结构奖励函数：遇到人体结构错误，如三只手、重要器官漂移。则惩罚。
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## 训练数据：
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经过以上函数算法，成功了提升了模型准确率，实测在输出中提升明显。
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## 训练中的发现：
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模型经过SFT冷启动后初步掌握了简单的思维链输出能力。经过RL训练思维链中扩展了非常多的思考过程。说明RL强化学习中，模型已经学会使用思维链进行思考。
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## 模型交流群
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## 训练过程输出
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## 幻觉问题
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## 自我学习中的有趣时刻-情绪保持能力
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## 自我学习中的有趣时刻-意识形态
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## 自我学习中的有趣时刻-潜在不安全因素，可能需要处理
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## 自我学习中的有趣时刻-强化学习中模型更加倾向于戏剧性输出-类似Carzy状态-算法需要优化
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## 自我学习中的有趣时刻-衍生自我认知为女性-可能涉及到社会学范畴
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## 自我学习中的有趣时刻-对于角色的深层理解
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## 暂时没有遇到“啊哈”时刻
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## 模型详情
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| 属性 | 规格 |
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|-------|------|
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| 基础架构 | Qwen2.5-7B |
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| 最大上下文 | 1024k |
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| 训练数据 | 0.1T小说 + 10万条SFT + MGRPO强化学习 |
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| 训练设备 | 2x8×H100 GPU集群 |
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| 训练时长 | 预计达到3000 H100小时 |
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| 量化支持 | GGUF（全系列量化计划中） |
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## 使用场景
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✅ **推荐场景**：
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- 角色扮演对话
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- 需要发散性思维的创意写作
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- 复杂逻辑的思维链（CoT）推理
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- 基于上下文的深度角色交互
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❌ **局限场景**：
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- 数学计算与代码生成
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- 短文本即时问答
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- 需要严格事实性的场景
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## 注意
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⚠ **需要严格遵循官方示例模板**：
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**返回的上下文需要去除思考标签与内容。否则将无法正确回复！**
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目前前端支持率非常低，建议手动修改前端代码。代码参考如下：
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```
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msg.role === 'assistant' ? {
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...msg,
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content: msg.content.replace(/<think>[\s\S]*?<\/think>/gi, '')
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}
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```
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**官方说明**
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[直达超链接](https://api-docs.deepseek.com/zh-cn/guides/reasoning_model)
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## 注意事项
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⚠️ 本模型使用数据包含小说版权内容及Tifa模型衍生数据，请遵守：
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1. 遵守apache-2.0
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2. 角色扮演数据需遵循[Tifa使用协议](https://leftnorth.com/terms.html)
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3. 生成内容需符合当地法律法规
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## 💡 使用建议
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**最佳实践**：
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```python
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# 启用角色扮演模式
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prompt = """你是一个小女孩/你是一个XX角色...
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<user>我走进门，看到你冲上来迎接我</user>
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<think>
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需要体现人物的气质
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加入环境描写
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保持对话风格
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</think>
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我看到XX进门..."""
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```
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**参数推荐**：
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```python
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generation_config = {
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"temperature": 0.75,
|
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"top_p": 0.6,
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"repetition_penalty": 1.08,
|
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"max_new_tokens": 1536,
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"do_sample": True
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}
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```
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## 致谢
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- Qwen系列模型提供的强大基座
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- Deepseek团队提供的研究思路
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- LeftNorth团队提供的技术支持
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- Tifa角色扮演模型的创新架构
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- HuggingFace社区的量化工具支持
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---
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license: apache-2.0
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---
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31
config.json
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{
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"_model_name_or_path": "Checkpoint 600 Merged",
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"_name_or_path": "./model/answer",
|
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"architectures": [
|
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
|
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"hidden_size": 3584,
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"initializer_range": 0.02,
|
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"intermediate_size": 18944,
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"max_position_embeddings": 1010000,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
|
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"pad_token_id": 151643,
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"rms_norm_eps": 9.999999747378752e-06,
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"rope_scaling": null,
|
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"rope_theta": 10000000.0,
|
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"sliding_window": null,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "float32",
|
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"transformers_version": "4.46.3",
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"use_cache": true,
|
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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{"task":"text-generation"}
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{
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"_from_model_config": true,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"pad_token_id": 151643,
|
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"transformers_version": "4.46.3"
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}
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merges.txt
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merges.txt
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model-00001-of-00007.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f7d2ed882367efdd282a605f20896c3b992c15cf7a58d9f37708e4979338cfca
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size 4976687216
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- '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 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": "<|endoftext|>",
|
||||
"eos_token_id": 151643,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"model_type": "gpt2",
|
||||
"pad_token": "<|endoftext|>",
|
||||
"pad_token_id": 151643,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user