Open-source large language models (LLMs) like Meta’s LLaMA-3 8B, with its 8 billion parameters, are designed to tackle complex language tasks such as sentiment analysis. In my latest Medium article, Elevating Sentiment Analysis: Fine-Tuning LLaMA 3 8B with Unsloth, I explore how to fine-tune LLaMA-3 8B for financial sentiment analysis using Unsloth, a library that simplifies and accelerates the training process.

In the article, you'll find:

• LLaMA-3 8B Overview: Understand the standout features and benefits of the LLaMA-3 8B model, including the controversies around fine-tune quantization performance.
• Custom Datasets: Learn to build datasets by mixing publicly available data with synthetic outputs. Get hands-on with code for large-scale generation.
• Fine-Tuning Workflow: Master the process of fine-tuning models for sentiment analysis using Unsloth notebooks, from setup to execution.
• GGUF Export: Discover how exporting to the General Graph Universal Format (GGUF) boosts performance and simplifies deployment.
• Ollama Deployment: Deploy custom GGUF models in Ollama for efficient inference. Explore specialized prompting techniques to enhance performance.
• Performance Insights: Compare different fine-tuned models using provided Python scripts. Evaluate performance objectively to find the best configurations.
• Evaluation: Present and measure the differences between quantizations and models like Mistral 7b and Dolphin-Mistral 7b 2.8.
• Anomalies Detection: Learn to spot and address anomalies despite thorough evaluations, ensuring your models’ reliability.

Discover the broader landscape of AI development and explore the detailed enhancements in the full article:

Elevating Sentiment Analysis: Fine-Tuning LLaMA 3 8B with Unsloth