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  1. Dictionary
    fine-tune
    /fʌɪnˈtjuːn/

    verb

    • 1. make small adjustments to (something) in order to achieve the best or a desired performance: "they can fine-tune the computer programs to focus on a small region of space"

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    More definitions, origin and scrabble points

  2. hatchworks.com › ai-inference-training-and-fine-tuningAI Inference vs Training vs Fine Tuning | What’s the Difference?

    Sep 6, 2024 · Learn the difference between inference, training, and fine-tuning AI with this guide based on my real experience and expertise. Plus follow my 5 steps to fine-tune an LLM.

  3. www.philschmid.de › fine-tune-multimodal-llms-with-trlHow to Fine-Tune Multimodal Models or VLMs with Hugging Face TRL

    2 days ago · 1. Define our multimodal use case. When fine-tuning VLMs, it's crucial to clearly define your use case and the multimodal task you want to solve. This will guide your choice of base model and help you create an appropriate dataset for fine-tuning. If you haven't defined your use case yet, you might want to revisit your requirements.

  4. openai.com › index › introducing-vision-to-the-fine-tuning-apiIntroducing vision to the fine-tuning API - OpenAI

    1 day ago · Today, we’re introducing vision fine-tuning (opens in a new window) on GPT-4o, making it possible to fine-tune with images, in addition to text. Developers can customize the model to have stronger image understanding capabilities which enables applications like enhanced visual search functionality, improved object detection for autonomous vehicles or smart cities, and more accurate medical ...

  5. www.geeksforgeeks.org › 7-steps-to-mastering-large-language7 Steps to Mastering Large Language Model Fine-tuning

    5 days ago · Step 2: Data Preparation for Fine-tuning. Step 3: Setting Up the Fine-tuning Environment. Step 4: Choosing the Right Base Model. Step 5: Fine-tuning Techniques. Step 6: Evaluating and Testing the Fine-tuned Model. Step 7: Setting up and Optimization of the Fine-tuned Model. Here in this article, we will discuss 7 Steps to Fine-Tuning of LLMs to ...

  6. neo4j.com › developer-blog › fine-tuning-vs-ragFine-Tuning vs. Retrieval Augmented Generation for LLMs

    Sep 11, 2024 · Fine-tuning an LLM involves a supervised training phase, where question-answer pairs are provided to optimize the LLM's performance. Conversely, in retrieval-augmented generation (RAG), the LLM serves as a natural language interface to access external information, which means it doesn't rely only on its internal knowledge to produce answers.

  7. arxiv.org › abs › 2409[2409.16223] Fine-Tuning is Fine, if Calibrated - arXiv.org

    Sep 24, 2024 · Fine-Tuning is Fine, if Calibrated. Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing valuable knowledge the model had learned in pre-training. For example, fine-tuning a pre-trained classifier capable of recognizing ...

  8. deepgram.com › learn › fine-tuning-your-aiFine-Tuning Your AI: Resources You Need - Deepgram

    Sep 18, 2024 · Fine-tuning is a technique for improving the performance and adaptability of pre-trained AI models by making adjustments to align them with specific tasks or domains. Choosing the right dataset for fine-tuning is crucial, considering domain relevance, data quality, licensing, accessibility, and benchmarking performance.