Fine-tuning allows users to adapt pre-trained LLMs to more specialized tasks.
By fine-tuning a model on a small dataset of task-specific data, you can improve its performance on that task while preserving its general language knowledge.
Fine-tuning allows you to take a pre-trained Large Language Model (LLM) and adapt it to your specific needs, improving its understanding and performance within specialized domains.
It helps customize its performance for a specific domain or task by training it on specialized data. This allows the model to excel in understanding and generating industry-specific language, concepts, and technical terms, making it more useful in specialized fields.
Fine-tuning ensures that your model is not just a generalist but becomes a powerful, domain-specific tool, capable of using the correct vocabulary, concepts, and nuances critical to your industry.
If you are interested in dig deeper in the fine-tuning, or if you are unfamiliar with this ecosystem have a look at this document
The goal of this repo is to provide one or more approaches for each use case we mention. Click on the link to each specific use case for a detailed deep dive.
For each use case we will try to provide a hands-on sample
Fine-tuning a model enables it to:
- Improve chatbot accuracy by tailoring the assistant to understand and respond with industry-relevant language.
- Understand industry-specific language (e.g., medical, legal, space, etc.),
using terminology and concepts unique to your field.
- It enables the model to adapt to the unique vocabulary and expertise of specific industries, ensuring it provides more accurate and relevant responses tailored to the field.
- Diagnose technical or mechanical issues, providing troubleshooting guidance based on
visual or textual input.
- Assess medical conditions by analyzing symptoms, diagnostic reports, or treatment plans.
- Evaluate and estimate car damagehelping with repair cost assessments.
- Analyze legal documents, summarizing cases or contracts with accurate legal terminology.
- Detect patterns in images
- Identify objects or specific features
- Evaluate product defects or manufacturing inconsistencies based on images
- Identify damages that requires maintenance assessing priorities (aviation )
- Identify abnormalities in medical scans
- Assist in scientific research by interpreting data from industries like aerospace or engineering.
This repository is a community effort, and we invite contributions, discussions, and ideas from everyone interested in fine-tuning LLMs! 🤝 Together, we will explore and document different configurations and approaches to help the entire community grow. Feel free to add your fine-tune use case and samples Stay tuned as we collaborate on this journey! 😄