Introduction to ChatGLM Efficient Tuning
ChatGLM Efficient Tuning is a project geared towards fine-tuning the ChatGLM-6B model, utilizing the PEFT (Parameter-Efficient Fine-Tuning) methods made available by Hugging Face. This exploration aims to enhance the model's performance with efficient tuning practices, offering a robust toolkit for training, evaluation, and inference via a user-friendly web interface.
Key Announcements
It's important to note that this repository is no longer actively maintained. Users and developers are directed to the LLaMA-Factory project for continued advancements and support in fine-tuning language models, including the newer ChatGLM2-6B.
Major Updates
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Web UI for Training and Evaluation: As of July 15, 2023, the project has developed an all-in-one web user interface, allowing users to fine-tune models directly in their web browser.
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FastEdit Release: On July 9, 2023, FastEdit was introduced as an efficient package for editing factual knowledge in large language models.
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Model Alignment with OpenAI: The project supports aligning the fine-tuned model with OpenAI’s API format for seamless integration into existing applications.
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Support for ChatGLM2-6B: Starting June 25, 2023, the framework allows for the fine-tuning of the ChatGLM2-6B model.
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4-bit LoRA Training: Introduced on June 5, 2023, this experimental feature supports training using 4-bit quantization for efficient resource use.
Data Collection and Usage
The project employs a diverse range of datasets for supervised fine-tuning, including multilingual datasets such as Stanford Alpaca, Open Assistant, and GPT-4 generated datasets. For reward modeling, datasets like HH-RLHF and Open Assistant are included. Some datasets require user confirmation and authentication through Hugging Face.
Fine-Tuning Techniques
The ChatGLM Efficient Tuning project supports several fine-tuning methods, including:
- LoRA: This method fine-tunes low-rank adapters within the model.
- P-Tuning V2: Adjusts the prefix encoder to optimize performance.
- Freeze: A method that fine-tunes only certain Multi-Layer Perceptrons (MLPs) in the final blocks of the model.
- Full Tuning: Involves adjusting all model parameters for optimal performance.
System Requirements
For seamless operation, the project requires Python 3.8+, PyTorch 1.13.1+, and other specific libraries like Transformers, Datasets, and PEFT. A powerful GPU is recommended for handling model training and tuning operations.
Setting Up and Running
- Data Preparation: Provides guidelines for customizing datasets, requiring updates to specific JSON files for seamless integration.
- Dependency Installation: A step-by-step guide on setting up the environment using commands for installing necessary libraries and dependencies.
- Single and Multi-GPU Fine-Tuning: Instructions are available for both single and multi-GPU settings, providing flexibility in handling hardware resources.
Demonstrations and Outputs
The project features various demo options, such as CLI, API, and web demos, to facilitate different presentation styles and use-case integrations. Evaluation processes incorporate metrics like BLEU and ROUGE to assess performance improvements.
Comparing to Other Implementations
ChatGLM Efficient Tuning sets itself apart by integrating many fine-tuning methods directly with Hugging Face’s transformers library, providing a concise and purely implemented alternative to other existing projects which might rely on different dependencies or frameworks.
In sum, ChatGLM Efficient Tuning presents a comprehensive yet efficient set of tools tailored for developers focused on enhancing and fine-tuning large language models, facilitating a more sophisticated performance aligned with the latest advancements in AI technology.