In this course, learn how to customize open-source AI models with one of the most common open-source models, LLaMa (Large Language Model Meta AI). Instructor Denys Linkov shares a hands-on approach to working with LLaMa, explaining LLaMa architecture, prompting, deploying, and training models. He uses a series of Python notebooks to show you how to adapt LLaMa to your use cases and employ it in an enterprise or startup environment.
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If you're building AI agents, you should get familiar with these 3 common agent/workflow patterns.
Let's break it down.
🔹 Reflection
You give the agent an input.
The agent then "reflects" on its output, and based on feedback, improves and refines.
Ideal tools to use:
- Base model (e.g. GPT-4o)
- Fine-tuned model (to give feedback)
- n8n to set up the agent.
🔹 RAG-based
You give the agent a task.
The agent has the ability to query an external knowledge base to retrieve specific information needed.
Ideal tools to use:
- Vector Database (e.g. Pinecone).
- UI-based RAG (Aidbase is the #1 tool).
- API-based RAG (SourceSync is a new player on the market, highly promising).
🔹 AI Workflow
This is a "traditional" automation workflow that uses AI to carry out subtasks as part of the flow.
Ideal tools to use:
- n8n to handle the workflow.
- GPT-4o, Claude, or other models that can be accessed through API (basic HTTP requests).
If you can master these 3 patterns well, you can solve a very broad range of different problems.
Let's break it down.
🔹 Reflection
You give the agent an input.
The agent then "reflects" on its output, and based on feedback, improves and refines.
Ideal tools to use:
- Base model (e.g. GPT-4o)
- Fine-tuned model (to give feedback)
- n8n to set up the agent.
🔹 RAG-based
You give the agent a task.
The agent has the ability to query an external knowledge base to retrieve specific information needed.
Ideal tools to use:
- Vector Database (e.g. Pinecone).
- UI-based RAG (Aidbase is the #1 tool).
- API-based RAG (SourceSync is a new player on the market, highly promising).
🔹 AI Workflow
This is a "traditional" automation workflow that uses AI to carry out subtasks as part of the flow.
Ideal tools to use:
- n8n to handle the workflow.
- GPT-4o, Claude, or other models that can be accessed through API (basic HTTP requests).
If you can master these 3 patterns well, you can solve a very broad range of different problems.
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AI (Artificial Intelligence) refers to machines simulating human intelligence 🧠, like reasoning, learning, and decision-making.
🖥📚 ML (Machine Learning) is a subset of AI, focused on algorithms that allow machines to learn from data and improve over time without being explicitly programmed.
AI thinks, ML learns. Simple as that!
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AI (Artificial Intelligence) refers to machines simulating human intelligence 🧠, like reasoning, learning, and decision-making.
🖥📚 ML (Machine Learning) is a subset of AI, focused on algorithms that allow machines to learn from data and improve over time without being explicitly programmed.
AI thinks, ML learns. Simple as that!
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Unlock the essentials of Artificial Intelligence (AI) with this free IBM course. Explore applications and key concepts like machine learning, deep learning, and neural networks.
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📖 We translate any PDF documents in one click
🛠 PDFMathTranslate is a free AI-powered tool for full-text translation of PDF documents.
🔹 Works very quickly - even a 200-page article can be translated in a minute
🔹 Completely preserves the text layout and does not make phrases clumsy
🔹 Knows 10 languages
🔗 Links: https://github.com/Byaidu/PDFMathTranslate
🛠 PDFMathTranslate is a free AI-powered tool for full-text translation of PDF documents.
🔰 Neural networks will translate books, articles, diagrams and graphs, preserving their presentable appearance
🔹 Works very quickly - even a 200-page article can be translated in a minute
🔹 Completely preserves the text layout and does not make phrases clumsy
🔹 Knows 10 languages
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🔅 LLM Foundations: Building Effective Applications for Enterprises
🌐 Author: Kumaran Ponnambalam
🔰 Level: Advanced
⏰ Duration: 1h 43m
📗 Topics: Large Language Models, Artificial Intelligence, Enterprise Software
📤 Join Artificial intelligence for more courses
🌀 Explore design considerations and best practices for building generative AI-powered applications at enterprise scale.
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As generative AI models have become increasingly popular, enterprises have started to build end-to-end applications to integrate their existing workflows with generative AI. In this course, instructor Kumaran Ponnambalam shows you how to get up and running with integration, performance management, trust, and monitoring to deliver effective and trustworthy generative AI applications at scale.Explore some of the unique characteristics and use cases for generative AI-powered applications in an enterprise setting, including available options, selection criteria, and key deployment considerations for generative AI models. Kumaran covers the basics of evaluating and fine-tuning models as well as patterns and best practices for core application design. By the end of this course, youll also be equipped with new skills to manage application performance, maintain safety and trust, and navigate some of the most important ethical and legal challenges of AI.
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With the rise of large language models (LLMs), fine-tuning for specific tasks has become more important than ever. But how can we do it efficiently without compromising performance? 🤔 Here are 5 advanced techniques that can help:
1. LoRA (Low-Rank Adaptation)
- LoRA reduces the number of trainable parameters by adding low-rank adaptation matrices, making fine-tuning faster and more memory-efficient.
2. LoRA-FA (LoRA with Feature Augmentation)
- This method combines LoRA with external feature augmentation, injecting task-specific features to further boost performance with minimal overhead.
3. Vera (Virtual Embedding Regularization Adaptation)
- Vera helps regularize model embeddings during fine-tuning, preventing overfitting and improving generalization across different domains.
4. Delta LoRA
- An extension of LoRA, this approach focuses on updating only the most significant layers, reducing computational costs while retaining fine-tuning effectiveness.
5. Prefix Tuning
- Instead of modifying model weights, this technique learns task-specific prefix tokens that steer the model’s output, enabling efficient adaptation to new tasks.
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