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WeClone

AI Agents & Assistants

A one‑stop tool that builds digital avatars from chat histories, supporting export, preprocessing, LLM fine‑tuning with image data, privacy filtering, and deployment across messaging platforms.

Python Latest v0.3.03 · 5mo ago Security brief →

Features

  • End‑to‑end pipeline for creating digital avatars from chat history
  • Fine‑tune large language models using both text and image modalities
  • Privacy‑focused data filtering with localized fine‑tuning and secure deployment
  • Multi‑platform integration (Telegram, upcoming WhatsApp, WeChat, Discord, Slack)
  • Flexible hardware options supporting full‑model training or low‑memory QLoRA

Recent releases

View all 1 releases →
v0.3.03 Breaking risk
⚠ Upgrade required
  • Update dependent packages: torchdata and torchaudio now require CUDA 12.6
  • llamafactory switched from git‑based install to a fixed version requirement
Breaking changes
  • Minimum required Python version changed to 3.12
Notable features
  • OnlineLLM with thread‑pooled batch chat and optional JSON‑guided decoding
  • New `add_relation` configuration option for toggling user relationship context in QA generation
  • Refactored CSV loading to support user relationship data from `users.json`
Full changelog

🎉 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:

  • 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torchdatatorchaudio,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。

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