Weights & Biases - Mastering model customization: fine-tuning Azure OpenAI service models with Weights & Biases
The session explores the concept of fine-tuning large language models using Azure OpenAI and Weights & Biases. Fine-tuning involves customizing pre-trained models with additional training on curated data to improve performance, accuracy, and adapt models to specific tasks or domains. The process includes selecting a base model, choosing a fine-tuning technique, preparing data, training, evaluating, and deploying the model. Fine-tuning can enhance model performance, reduce costs, and mitigate risks by aligning models with specific organizational needs. Practical applications include healthcare documentation, legal compliance, customer service, and more. The session also discusses the integration of Weights & Biases for tracking and evaluating fine-tuning processes, emphasizing the importance of data quality and evaluation metrics. Examples from Microsoft Cloud for Healthcare and Harvey illustrate successful fine-tuning applications, demonstrating improved efficiency and performance in real-world scenarios.
Key Points:
- Fine-tuning customizes pre-trained models for specific tasks, improving performance and accuracy.
- Azure OpenAI supports various fine-tuning techniques, including supervised fine-tuning and reinforcement learning.
- Weights & Biases provides tools for tracking and evaluating fine-tuning processes, ensuring data quality and effective model evaluation.
- Fine-tuning applications include healthcare, legal compliance, customer service, and more, offering enhanced accuracy and cost efficiency.
- Successful fine-tuning requires a well-prepared, heterogeneous dataset and careful evaluation to avoid overfitting.
Details:
1. 🎤 Welcome and Agenda Overview
- Introduction of speakers: Alicia Frame, Lead for Fine Tuning on Azure; Chris Finel, Co-founder and CISO for Weights and Biases.
- Excitement expressed for the session focusing on model customization with fine-tuning using Azure OpenAI and Weights and Biases.
- Engagement encouraged by asking attendees to put questions in the chat for discussion at the end of the presentation.
- Focus on providing a demo as a central part of the session to showcase practical applications.
2. 🔍 Deep Dive into Fine-Tuning
- Fine-tuning enables models to be customized for specific industry applications, improving their relevance and effectiveness.
- Azure OpenAI and Weights and Biases offer tools that streamline the fine-tuning process, making it accessible to a broader audience.
- A hands-on demo illustrates how fine-tuning can significantly enhance model quality by adapting it to specific needs.
- The session covers the role of agents in the fine-tuning process, providing an opportunity for participants to ask questions and deepen their understanding.
3. 🧠 Fine-Tuning Process with Azure and Weights & Biases
3.1. Overview of Fine-Tuning Methods
3.2. Applications and Impact of Fine-Tuning
4. 🔧 Essential Tools and Techniques for Fine-Tuning
- Identify specific use cases and requirements before selecting a base model. For instance, for a low latency chat model, consider using models like 40 Mini in Azure OpenAI.
- Different fine-tuning techniques serve distinct purposes: Supervised fine-tuning is ideal for scenarios requiring model copying, DPO is suited for aligning models with user preferences, and reinforcement fine-tuning is beneficial for developing new reasoning capabilities.
- Fine-tuning involves a multi-step process: Prepare high-quality datasets, conduct training, perform evaluations, deploy, and continuously monitor the model's performance. This process requires iterative refinement based on initial results.
- The quality of datasets is pivotal; imbalanced datasets can bias model performance. Ensure the dataset reflects diverse and balanced examples for accurate model training.
- Evaluation poses challenges, especially for models with multiple valid outputs. Establishing reliable performance metrics is key to effective model iteration and updates.
- Continuous iteration is essential as new models and data become available. Implement robust evaluation metrics to guide decisions when replacing existing models.
- The Weights and Biases platform facilitates this workflow, underscoring the importance of evaluation and iterative improvement in fine-tuning.
- A significant portion of fine-tuning efforts is dedicated to non-training steps, illustrating the complexity of developing and deploying effective models.
5. 🛠️ Navigating Fine-Tuning Challenges and Workflow
- The journey of model customization starts with prompt engineering and progresses through retrieval-augmented generation (RAG) and fine-tuning, ultimately leading to building a model from scratch if necessary.
- Prompt engineering helps identify whether retrieval or fine-tuning is needed by revealing areas where the model lacks domain-specific knowledge or struggles with style and tone.
- RAG and fine-tuning are often used together for model customization, allowing for more effective results without the need to build a new model from scratch.
- Building a new foundation model from scratch is a highly advanced technique and is often unnecessary due to the availability of off-the-shelf models like LLaMA or OpenAI.
- The process of model development can start quickly with prompt engineering, allowing for rapid prototyping and iterative improvements by increasing complexity gradually.
- Fine-tuning can be used to distill information into smaller, more economical models when deploying to a larger user base.
- Advanced pre-training and model distillation are typically best left to those with significant resources, as they require substantial investment.
6. 🔄 Integrating Fine-Tuning Across Applications
6.1. Hybrid Fine-Tuning Use Cases
6.2. Benefits of Fine-Tuning Strategies
7. 🏥 Industry Applications: Healthcare, Legal, and More
7.1. Healthcare and Life Sciences
7.2. Translation and Dialects
7.3. Finance and Banking
7.4. Legal and Compliance
7.5. Customer Service Applications
7.6. Computer Programming
8. 📈 Success Stories: Microsoft and Harvey
8.1. Microsoft Cloud for Healthcare
8.2. Harvey in the Legal Industry
9. 🔗 Enhancing Fine-Tuning with Weights & Biases
- Weights & Biases provides a centralized platform for managing model fine-tuning, including data set tracking and experiment evaluation.
- Customers face issues with manually tracking experiments, leading to potential disorganization; Weights & Biases addresses this by offering a single source of truth for all fine-tuning activities.
- The platform supports the growing data requirements for fine-tuning by maintaining a central registry for data sets and their evolution.
- Regulatory compliance is facilitated through the system's ability to track data set lineage and model production processes.
- Integration with Azure AI allows Weights & Biases users to see their fine-tuning runs directly within the Azure console, ensuring all modeling efforts are consolidated in one place.
10. 🚀 Live Demo: Crafting a Fine-Tuned Chat Bot
- Fine-tuning of models can be efficiently managed using Weights and Biases, allowing for tracking and iteration on model performance metrics.
- Azure AI Foundry and Weights and Biases can be integrated to enhance model workflows, providing a cohesive environment for development.
- The process of fine-tuning a chat support bot involves defining specific business needs and tailoring the model to meet those needs using synthetic data, which can be sourced from companies like Bitext.
- Prompt engineering can add complexity and increase token usage and latency, suggesting fine-tuning as a more efficient approach.
- Human and LM (Language Model) annotation techniques can be used to create a customized dataset that reflects desired business communication styles.
- Fine-tuning can improve response quality by ensuring the bot provides concise and relevant information, aligning with business requirements.
- The demonstration highlighted the importance of comparing fine-tuned models against base models using metrics such as fluency, semantic similarity, and token usage.
- Fine-tuned models demonstrated higher quality responses, reduced token usage, and faster processing times, leading to cost efficiency.
- Weights and Biases platform allows for continuous fine-tuning and evaluation, providing a centralized system to manage multiple model versions and performance metrics.
11. 🤖 Fine-Tuned Models in Agent Workflows
- Fine-tuned models can be strategically integrated into agent workflows to enhance specific task performance, leading to improved efficiency and accuracy.
- An orchestrator can effectively route requests to various models, including fine-tuned ones, ensuring that each task utilizes the most appropriate model.
- Models tailored for tool use can significantly enhance the accuracy of tool invocation, reducing errors and improving outcomes.
- Utilizing conversation threads from agents to fine-tune models can lead to improved interaction tracking and response accuracy, directly benefiting customer service and support operations.
- For example, fine-tuned models have shown to increase tool usage accuracy by 25% and reduce error rates by 30% in customer support scenarios.
12. 📏 Conclusion and Q&A
- The size of the training data set for fine-tuning a pre-trained LLM can be surprisingly small; around 100 examples can be sufficient, depending on the complexity of the task and the diversity of the data set.
- For simple tasks, a smaller data set is adequate, but for domain adaptation, tens of thousands to hundreds of thousands of examples may be necessary.
- Ensuring a heterogeneous and well-represented data set is crucial to avoid overfitting during fine-tuning.
- Azure OpenAI offers low-rank adaptation, updating only 1.5% to 2% of parameters, which makes training more efficient and cost-effective.
- Full fine-tuning, which involves adjusting all model weights, is available through platforms like Hugging Face and Azure ML.
- Low-rank adaptation allows for faster training, smaller deployment footprints, and cost savings, making it suitable for custom applications on limited hardware resources.