🎉 What's Changed

The key highlights of this update include an upgrade to Python 3.12 and optimization of the dataset pipeline.

Dependency and Environment Updates:

• Upgraded the required Python version to 3.12 in pyproject.toml and development settings, and updated the target version for linting and type checking to Python 3.12. [1] [2] [3]
• Updated dependencies: switched from a git-based install of llamafactory to a fixed version, added torchdata and torchaudio with CUDA 12.6 support, and refined platform-specific dependency markers for PyTorch packages. [1] [2]

Data

• Added the "<begin_chat>" marker in user messages, allowing for improved context in conversation flows.
• Updated the qa_generator.py to include a new mechanism for managing chat member relationships, allowing the addition of contextual information about the relationship between users in conversations.
• Refactored the CSV loading function to support loading user relationship data from a users.json file, improving the context provided during QA generation.
• Added a new configuration option add_relation to the dataset settings, enabling users to toggle this feature.

others

• Introduces OnlineLLM with thread‑pooled batch chat and optional JSON‑guided decoding; unifies JSON parsing across vLLM and OpenAI results.

• fix: fix triton source from default cuda129 to 126 by @MapleWithered in https://github.com/xming521/WeClone/pull/198

New Contributors

• @MapleWithered made their first contribution in https://github.com/xming521/WeClone/pull/198

Full Changelog: https://github.com/xming521/WeClone/compare/v0.3.02...v0.3.03

😊 更新内容

本次更新核心亮点包括升级至Python 3.12以及数据集管线优化。

依赖与环境更新：

• 在pyproject.toml和开发配置中将Python版本升级至3.12。
• 依赖项更新：将llamafactory从基于git的安装方式改为固定版本，新增支持CUDA 12.6的torchdata和torchaudio，并优化了PyTorch包的平台特定依赖标记。

数据处理

• 新增"<begin_chat>"标记，以提升对话流程的上下文连贯性
• 更新qa_generator.py，新增聊天成员关系管理机制，支持在对话中添加用户间关系的上下文信息
• 重构CSV加载函数，支持从users.json文件加载用户关系数据，增强问答生成时的上下文信息
• 在数据集配置中新增add_relation选项，允许用户自主启用/禁用此功能

其他

• 引入支持线程池批量聊天和可选JSON引导解码的OnlineLLM；统一了vLLM与OpenAI结果的JSON解析流程。