WeClone

[!IMPORTANT]

Telegram is now supported as a data source !

✨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

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

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
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, Telegram, Slack, Feishu and other platforms.

Usage steps:

1. Deploy AstrBot
2. Deploy messaging platforms like Discord, Telegram, Slack in AstrBot
3. Execute weclone-cli server to start the API service
4. Add a new service provider in AstrBot, select OpenAI type, fill in the API Base URL according to AstrBot's deployment method (e.g., for docker deployment it might be http://172.17.0.1:8005/v1), fill in the model as gpt-3.5-turbo, and enter any API Key
5. Tool calling is not supported after fine-tuning, please turn off the default tools first by sending the command: /tool off_all on the messaging platform, otherwise the fine-tuned effect won't be visible.
6. Set the system prompt in AstrBot according to the default_system used during fine-tuning.

[!IMPORTANT] Check the api_service logs to ensure that the large model service request parameters are consistent with those used during fine-tuning as much as possible, and turn off all tool plugin capabilities.

LangBot

LangBot is an easy-to-use open-source LLM chatbot platform suitable for various scenarios. It connects to various global instant messaging platforms. You can set up your IM bot in just 5 minutes.

1. Deploy LangBot
2. Add a bot (Discord, Telegram, Slack, Lark e.g.) in LangBot
3. Execute weclone-cli server to start the WeClone API service
4. Add a new model in the model page, name it gpt-3.5-turbo, select OpenAI as the provider, fill in the request URL as WeClone's address. For detailed connection methods