Fine-tuning an LLM judge to reduce hallucination

Introduction

Introduction

Hello everyone! Welcome to this live seminar on fine-tuning an LLM (Large Language Model) judge to reduce hallucination. I'm Thomas, an ML engineer at Weights & Biases. Joining me today is Sophia Yang, Head of Relations Developer Relations at Mistral.

We are here to share how to fine-tune an LLM judge using Mistral's impressive AI products and how to leverage Weights & Biases tools for tracking and evaluating your fine-tuning process. This detailed tutorial will cover the steps to fine-tune an LLM and use it effectively to reduce hallucinations and improve output quality.

About Mistral AI

Sophia Yang gave a comprehensive introduction to Mistral AI:

• Mistral AI is a team of 50+ scientists and entrepreneurs based in Paris, aiming to build the world's most efficient large language models and tools for developers and businesses globally.
• Key milestones include the release of Mistral 7B (September 2023), Mistral Mixof Experts Model Mixol A x7B (December 2023), and Mistral's optimized models (February 2024).
• Mistral AI also offers customizable options like fine-tuning API and fine-tuning codebase.
• Recently released M models include Mistral MATH for math-specific tasks and Coastal Mamba for code generation.

What is Fine-Tuning?

Fine-tuning involves taking a pre-trained model and adapting it for a specific task by training it on a smaller, task-specific dataset. Mistral uses optimized LoRA (Low-Rank Adaptation) fine-tuning, which is efficient and cost-effective.

Why Fine-Tune?

Fine-tuning can significantly boost performance for specific tasks and domains, especially for smaller models mimicking the performance of much larger models. It is beneficial for tasks requiring specific tones, formats, styles, or better alignments.

Fine-Tuning with Mistral's API

To fine-tune using Mistral's API, follow these steps:

1. Prepare Your Dataset: Format it into JSONL as specified in the documentation.
2. Upload the Dataset: Use Mistral's API.
3. Create a Fine-Tuning Job: Configure the parameters and start fine-tuning.
4. Use Your Fine-Tuned Model: Wait for completion and leverage the fine-tuned model.

Hands-On Tutorial

Overview

Today’s tutorial involves:

• Loading and understanding the dataset.
• Creating initial prompts for the task.
• Fine-tuning a Mistral model.
• Evaluating the performance of fine-tuned models.

Setting Up

First, clone the GitHub repository and ensure you have datasets ready. Here's the structure:

1. Load Data
2. Initial Prompt Creation
3. Mistral Client Setup

Evaluating Pre-Trained Models

We created a prompt for detecting hallucinations and ran evaluations on the Mistral 7B model. Using Weave, we tracked predictions and metrics for the evaluations.

Fine-Tuning Process

1. Data Preparation: Format training and validation datasets.
2. Uploading Datasets: Upload JSONL datasets to the Mistral server.
3. Creating Fine-Tuning Jobs: Configure and initiate fine-tuning jobs using the Weights & Biases API integration.
4. Immediate Availability: Once tuned, the model is immediately available for use.

Results and Improvement

The initial pre-tuned models showed decent performance. However, after fine-tuning, the model performed significantly better, achieving higher accuracy and F1 scores. We also explored two-step fine-tuning by combining domain-specific data with a generic dataset, which further enhanced the model's ability to reduce hallucinations.

Conclusion

This tutorial demonstrated how to effectively fine-tune an LLM judge using Mistral's robust API and the tools offered by Weights & Biases. Fine-tuning significantly improves model performance for domain-specific tasks, and with efficient tracking tools like Weave, it ensures precise and trackable improvements.

Keywords

• Fine-Tuning
• Large Language Model (LLM)
• Mistral AI
• Weights & Biases
• Hallucination Detection
• LoRA

FAQ

Q: What is Mistral AI? A: Mistral AI is a team based in Paris focusing on building efficient large language models and tools for developers and businesses. They provide models and fine-tuning services.

Q: What is fine-tuning? A: Fine-tuning involves adapting a pre-trained model to a specific task by training it on a smaller, task-specific dataset. Mistral uses optimized LoRA fine-tuning.

Q: How does Mistral’s fine-tuning API work? A: The API involves preparing your dataset in a specified format, uploading it, creating a fine-tuning job, and then using the fine-tuned model once it's ready.

Q: What is Weave? A: Weave is a tool by Weights & Biases that helps track interactions with LLMs by tracing the inputs and outputs of API calls, useful for debugging and evaluation.

Q: Can I use fine-tuned models immediately? A: Yes, once the fine-tuning job is complete, the model is immediately available for use via the Mistral API.