5 Steps to Build Language Models Apps
How to select the right model and approach...
Good morning fellow AI enthusiast! This is the sixth video of my series for our free course "Training & Fine-Tuning LLMs for Production"!
Dive into the world of Large Language Models (LLMs) with the essential steps to build, refine, and deploy AI-powered applications.
Learn about selecting the right LLM, tailoring it to your needs, evaluating its performance, and overcoming deployment challenges.
Here are some practical insights and tools for LLM-based businesses...
1️⃣ 5 Essential Steps to Building Language Models Apps
Let's dive right in. Here are the steps you are looking for:
Choose the Right LLM: Select from proprietary models (like GPT4), open-source LLMs (like Llama, Mistral), or develop your own. Consider performance, knowledge cutoff, and infrastructure costs.
Customize the Model: Adapt the LLM to your needs using techniques like fine-tuning, RLHF, RLAIF, or RAG, each suitable for different tasks.
Evaluate Performance: Use benchmarks and A/B testing to assess the model's effectiveness, considering the subjective nature of text outputs (specific examples in the video).
Deployment: Tackle challenges like computational power, memory, and cost. Use methods like model distillation and quantization for efficiency, keeping in mind ethical and privacy considerations.
Monitor and Refine Post-Deployment: Continuously oversee the model to manage bugs and unexpected behaviors. Tools like Weights and Biases can assist in this ongoing process.
Learn more in the video:
2️⃣ More about our course in collaboration with Towards AI, Activeloop, and the Intel Corporation disruptor initiative!
Tl;dr: The course is about showing everything about LLMs (train, fine-tune, use RAG…), and it is completely free!
Is the course for you?
If you want to learn how to train and fine-tune LLMs from scratch, and have intermediate Python knowledge, you should be all set to take and complete the course.
This course is designed with a wide audience in mind, including beginners in AI, current machine learning engineers, students, and professionals considering a career transition to AI.
We aim to provide you with the necessary tools to apply and tailor Large Language Models across a wide range of industries to make AI more accessible and practical.
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