Introduction to chatglm_finetuning Project
Overview
The chatglm_finetuning project is focused on the fine-tuning of ChatGLM models, which have various quantization levels including standard and optimized versions for performance and efficiency. The project is hosted on GitHub under the ssbuild organization and is a part of the broader deep_training initiative. It supports a range of training and inference techniques to optimize ChatGLM models according to specific user needs.
Key Features
Support for Various Trainers
The project integrates support for different training platforms such as PyTorch Lightning, ColossalAI, and Transformers, enabling flexibility in choosing the infrastructure that best fits user requirements. The addition of Accelerator support further enhances the capability to handle large models and datasets efficiently.
Quantization and Optimization
ChatGLM models can be fine-tuned at different quantization levels, ranging from FP16 to INT8 and INT4. This flexibility allows users to adopt models according to their system capabilities, targeting a balance between performance and resource usage.
LoRA Fine-tuning
The project incorporates tools for LoRA and AdaLoRA fine-tuning, which are modern techniques for optimizing large models with limited resources. It includes scripts and configurations to adjust model parameters efficiently.
Installation
To install the necessary dependencies, users can run the following command:
pip install -U -r requirements.txt
If there are issues with installation, switching to the official PyPI source might resolve these:
pip install -i https://pypi.org/simple -U -r requirements.txt
Data Handling
The project offers support for both standard and customized data formats, facilitating the creation and management of datasets required for training. This adaptability ensures the project can accommodate a range of data input styles.
Inference
The project includes scripts for different types of inference to cater to various fine-tuning models:
infer.pyfor pre-trained models.infer_finetuning.pyfor fine-tuned models.infer_lora_finetuning.pyfor LoRA fine-tuned models.
The memory requirements for different quantization levels are provided, ensuring users can choose an appropriate setup based on GPU availability.
Training
The project provides scripts tailored for full parameter training, LoRA training, and P-Tuning V2, allowing users to execute complex model training processes efficiently. The scripts are designed for easy adjustment, supporting customization according to specific needs.
Community and Support
The chatglm_finetuning project is part of a larger suite of fine-tuning projects available under the ssbuild organization, each catering to various LLMs and training methodologies. This robust community support provides a collaborative environment for users and developers to innovate and share insights.
Forward-Looking Approach
The project continually updates to incorporate the latest methodologies and improvements in the field, ensuring it remains a relevant and powerful tool for model fine-tuning.
For more information or to explore the source code, users are encouraged to visit the repository on GitHub: chatglm_finetuning.