OpenSyntheticCC is a repository for fine-tuning language models on synthetic Chain-of-Thought (CoT) and code datasets. It provides scripts and configurations for distributed training, especially with DeepSpeed, and supports large-scale supervised fine-tuning.

• Fine-tuning on synthetic CoT and code datasets
• Distributed training with DeepSpeed and torchrun
• Customizable training parameters via shell scripts
• Data collation and tokenization for instruction-following tasks
• Example scripts for quick start

1. Clone the repository:

   git clone https://github.com/your-username/OpenSyntheticCC.git
   cd OpenSyntheticCC

2. Install dependencies:

   pip install -r requirements.txt

• Format: JSONL, each line should contain instruction and response fields.
• Note: Due to privacy reasons, the dataset is not open-sourced for now. In the future, we will release scripts for generating synthetic datasets.

• Edit sft.sh to set your model path, data path, and output directory.
• Run the script:

  bash sft.sh

• The script uses torchrun and DeepSpeed for distributed training. Training parameters (batch size, learning rate, etc.) can be modified in sft.sh.

• You can also run finetune.py directly:

  python finetune.py --model_name_or_path <MODEL_PATH> --data_path <DATA_PATH> --output_dir <OUTPUT_DIR> ...

• See sft.sh for a full example of arguments.

• DeepSpeed configuration is provided in deepspeed.json.
• The script supports multi-node and multi-GPU training.

• finetune.py: Main training script for supervised fine-tuning.
• sft.sh: Example shell script for distributed training.
• deepspeed.json: DeepSpeed configuration for efficient large model training.
• git.sh: Helper script for quick git add/commit/push.
• .gitignore: Ignore logs, archives, and Java-related files.

Contributions are welcome! Please open an issue or submit a pull request.

This project is licensed under the MIT License.