Open-Instruct: Open Instruction-Following Language Models
Open-Instruct is an innovative project aiming to fine-tune popular pretrained language models using publicly available datasets, enhancing their ability to follow instructions. It provides a platform containing all necessary resources for instruction-tuning, including cutting-edge techniques, datasets in a standardized format, and comprehensive evaluation codes.
Purpose and Goals
The project's main focus is on fine-tuning and improving language models so they can better grasp and execute given instructions. It includes:
- Finetuning Code: Offering code to refine language models using the latest techniques.
- Evaluation Code: Providing evaluation tools to assess these models on various benchmarks, ensuring they excel in numerous aspects.
- Model Checkpoints: Sharing checkpoints and other significant artifacts used throughout the project.
Research Background
Research forms the foundation of Open-Instruct, with three pivotal papers driving its progress:
- "How Far Can Camels Go?": This paper examines the current state of instruction tuning on open resources.
- "Camels in a Changing Climate": This study discusses leveraging Llama-2 models and preference optimization for better adaptation.
- "Unpacking DPO and PPO": This paper disentangles best practices for learning from preference feedback.
Recent Updates
The project is continuously evolving. Here are some of the recent updates:
- Use of advanced libraries: Integration of the Alpaca-Eval library to enhance evaluation processes.
- Broadened Dataset Support: New datasets such as LIMA and WizardLM have been incorporated, reflecting commitment to diverse and comprehensive tuning.
- Enhanced Model Compatibility: Now supports LLaMa 2 finetuning, FlashAttention-2, and other model types, contributing to speedier evaluations.
Technical Setup
Open-Instruct offers flexible setup options:
- Bare Environment Installation: Involves installing Cuda and PyTorch, followed by required packages.
- Docker Setup: Allows for running experiments in a Docker environment, making the process more streamlined for users.
Training and Finetuning
The training process includes preparing datasets, adequately formatting them, and ensuring models are compatible. Finetuning is done using state-of-the-art methods such as DeepSpeed and Accelerate frameworks. Users are encouraged to manipulate loss calculations to maximize results and efficiency.
Parameter-Efficient Finetuning
For users looking for resource-efficient solutions, Open-Instruct supports LoRA and QLoRA finetuning. These techniques reduce the number of parameters needed for training, making it more cost-effective and faster.
Model Evaluation
Evaluation is crucial. Open-Instruct provides scripts for assessing model performance on established benchmarks, such as MMLU and TruthfulQA, to ensure models are capable of handling various linguistic challenges.
Released Models and Checkpoints
Open-Instruct has released multiple checkpoints accessible to the public. The models can be found on platforms like HuggingFace, allowing the community to apply these improved language models in various domains.
Licensing and Contributions
Open-Instruct operates under open-source licenses, providing transparency and encouraging community involvement. Contributors are invited to enhance the project further by adhering to the provided guidelines and using the standardized format.
Conclusion
Open-Instruct is revolutionizing the way language models are tuned for instruction-following. By offering comprehensive resources and ongoing updates, this project is paving the way for more intuitive and efficient machine interactions. Researchers and developers alike are encouraged to delve into Open-Instruct to advance their initiatives with cutting-edge tools and insights.