WeClone

<h3 align="center">🚀 One-stop solution for creating your digital avatar from chat history 💡</h3> <div align="center">

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</div> <p align="center"> <a href="https://github.com/xming521/WeClone/blob/master/README_zh.md" target="_blank">简体中文</a>｜ English</a>｜ <a href="https://www.weclone.love/" target="_blank"> Project Homepage </a> ｜ <a href="https://docs.weclone.love/docs/introduce/what-is-weclone.html" target="_blank"> Documentation </a> </p>

✨Core Features

• 💫 Complete end-to-end solution for creating digital avatars, including chat data export, preprocessing, model training, and deployment
• 💬 Fine-tune LLM using chat history with support for image modal data, infusing it with that authentic "flavor"
• 🔗 Integrate with Telegram, WhatsApp (coming soon) to create your own digital avatar
• 🛡️ Privacy information filtering with localized fine-tuning and deployment for secure and controllable data

📋Features & Notes

Data Source Platform Support

| Platform | Text | Images | Voice | Video | Animated Emojis/Stickers | Links (Sharing) | Quote | Forward | Location | Files | |----------|------|--------|-------|-------|-----------------|-----------------|-------|---------|----------|-------| | Telegram | ✅ | ✅ | ❌ | ❌ | ⚠️Convert to Emoji | ❌ | ❌ | ✅ | ✅ | ❌ | | WhatsApp | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | | Discord | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | | Slack | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 | 🚧 |

Deployment Platform Support

| Platform | Deployment Support | |----------|--------------------| | Telegram | ✅ | | WhatsApp | 🚧 | | WeChat (Personal Account) |✅ (Based on openclaw-weixin)| | Discord | ✅ | | Slack | ✅ |

[!IMPORTANT]

• WeClone is still in rapid iteration phase, current performance does not represent final results.
• LLM fine-tuning effectiveness largely depends on model size, quantity and quality of chat data. Theoretically, larger models with more data yield better results.
• The performance of the 7B model is average, while models with 14B or more parameters tend to deliver better results.
• Windows environment has not been rigorously tested. You can use WSL as the runtime environment.

Recent Updates

[25/07/10] Data source added Telegram
[25/06/05] Support for image modal data fine-tuning

Hardware Requirements

The project uses Qwen2.5-VL-7B-Instruct model by default with LoRA method for SFT stage fine-tuning. You can also use other models and methods supported by LLaMA Factory.

Estimated VRAM requirements: | Method | Precision | 7B | 14B | 30B | 70B | xB | | ------------------------------- | --------- | ----- | ----- | ----- | ------ | ------- | | Full (bf16 or fp16) | 32 | 120GB | 240GB | 600GB | 1200GB | 18xGB | | Full (pure_bf16) | 16 | 60GB | 120GB | 300GB | 600GB | 8xGB | | Freeze/LoRA/GaLore/APOLLO/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 2xGB | | QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | xGB | | QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | x/2GB | | QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | x/4GB |

Environment Setup

1. CUDA installation (skip if already installed, requires version 12.6 or above)

2. It is recommended to use uv to install dependencies, which is a very fast Python environment manager. After installing uv, you can use the following commands to create a new Python environment and install dependencies.

git clone https://github.com/xming521/WeClone.git && cd WeClone
uv venv .venv --python=3.12
source .venv/bin/activate # windows .venv\Scripts\activate
uv pip install --group main -e . 

3. Copy the configuration file template and rename it to settings.jsonc, and make subsequent configuration changes in this file:

cp examples/tg.template.jsonc settings.jsonc

[!NOTE] Training and inference related configurations are unified in the file settings.jsonc

4. Use the following command to test whether the CUDA environment is correctly configured and can be recognized by PyTorch (not needed for Mac):

  python -c "import torch; print('CUDA Available:', torch.cuda.is_available());"

5. (Optional) Install FlashAttention to accelerate training and inference: uv pip install flash-attn --no-build-isolation.

Model Download

It is recommended to use Hugging Face to download models, or use the following command:

git lfs install
git clone https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct models/Qwen2.5-VL-7B-Instruct

Data Preparation

Please use Telegram Desktop to export chat records. Click the top right corner in the chat interface, then click "Export chat history". Select Photos for message types and JSON for format. You can export multiple contacts (group chat records are not recommended), then place the exported ChatExport_* in the ./dataset/telegram directory, meaning put different people's chat record folders together in ./dataset/telegram.

Data Preprocessing

• First, modify the language, platform, and include_type in the configuration file according to your needs.
• If you use telegram, you need to modify the telegram_args.my_id in the configuration file to your own telegram user ID.
• By default, the project uses Microsoft Presidio to remove phone numbers, email addresses, credit card numbers, IP addresses, geographic location names, international bank account numbers, cryptocurrency wallet addresses, age information, and generic ID numbers from the data, but it cannot guarantee 100% identification.
• Therefore, a blocklist blocked_words is provided in settings.jsonc, allowing users to manually add words or phrases they want to filter (the entire sentence containing blocked words will be removed by default).

[!IMPORTANT] 🚨 Please be sure to protect personal privacy and do not leak personal information!

• Execute the following command to process the data. You can modify the make_dataset_args in settings.jsonc according to your own chat style.

weclone-cli make-dataset

More Parameter Details: Data Preprocessing

Generate a User Profile in One Command

Run from the project root:

weclone-cli build-profile

Choose profiles only (the default) or include events. The results may overlap; choose profiles only to save tokens. You can also select the mode directly:

weclone-cli build-profile --profile-only
weclone-cli build-profile --with-events

Results are saved under dataset/res_csv/agent/profile_runs/, and the profile path is printed on completion. Use --output-dir <new-directory> to choose the result directory. API models require --max-context-tokens <limit> to set the input token limit.

Run weclone-cli server to view and review profiles, or weclone-cli server --source <profile-path> to select a profile.

Data Encryption and Passwords

Set security_args in settings.jsonc:

"security_args": {
    "storage_mode": "encrypted"
}

New templates default to encrypted; choose plaintext for plaintext storage. Older configurations without this setting use plaintext. Encrypted mode does not yet support distributed training.

Set a shared password at the first encrypted task or web visit. Use it to unlock later tasks and sign in to the web interface.

Encrypted outputs end in .enc. Protect your original chats and any plaintext exports. Keep the .weclone/ directory: it contains the key needed to read encrypted data.

Export plaintext, change a password, or reset a forgotten password:

weclone-cli decrypt --input <encrypted-file> --output <new-plaintext-file>
weclone-cli security-change-password  # Requires the old password; preserves data
weclone-cli server-reset-password     # Reset a forgotten password

Encrypted data cannot be recovered without the password. After resetting it or switching storage modes, regenerate data from the original chats in a new output directory.

Configure Parameters and Fine-tune Model

• (Optional) Modify model_name_or_path, template, lora_target in settings.jsonc to select other locally downloaded models.
• Modify per_device_train_batch_size and gradient_accumulation_steps to adjust VRAM usage.
• You can modify parameters like num_train_epochs, lora_rank, lora_dropout in train_sft_args based on your dataset's quantity and quality.

Single GPU Training

weclone-cli train-sft

Multi-GPU Training

Uncomment the deepspeed line in settings.jsonc and use the following command for multi-GPU training:

uv pip install "deepspeed<=0.16.9"
deepspeed --num_gpus=number_of_gpus weclone/train/train_sft.py

Simple Inference with Browser Demo

Test suitable temperature and top_p values, then modify infer_args in settings.jsonc for subsequent inference use.

weclone-cli webchat-demo

Inference Using API

weclone-cli server --inference --port 8005

Test with Common Chat Questions

Does not include questions asking for personal information, only daily conversation. Test results are in test_result-my.txt.

weclone-cli server --inference --port 8005
weclone-cli test-model

🖼️ Results Showcase

[!TIP] We're looking for interesting examples of native English speakers chatting with WeClone! Feel free to share them with us on Twitter.

🤖 Deploy to Chat Bots

AstrBot

AstrBot is an easy-to-use multi-platform LLM chatbot and development framework ✨ Supports Discord, Telegr