This repository contains the latest techniques for developing state-of-the-art language models responsible for the recent boom in generative AI models, like OpenAI's GPT-4, Meta's LLaMA 2, Mistral-7B, and Anthropic's Claude.
Mastering deep learning with PyTorch to discover how neural networks can be used to model patterns in unstructured data, such as text. Discover how the transformer architecture has revolutionized text modeling, and build your own transformer model from scratch! Finally, learn to work with and fine-tune pre-trained LLMs available from Hugging Face. Let’s dive into the details:
| Model | Parameters (Billion) | Strengths | Weaknesses |
|---|---|---|---|
| Bard (Google AI) | 137 | - Factual language understanding: Proficient at comprehending factual information. | - Limited access: Not widely accessible to the public. May be conservative in outputs. |
| GPT-4 (OpenAI) | Unknown (estimated 100T+) | - Text generation: Capable of creative writing and generating diverse textual content. | - Not yet publicly available: Details about its capabilities remain undisclosed. |
| Jurassic-1 Jumbo (AI21 Labs) | 178 | - Large factual knowledge base: Rich repository of factual information. | - Prone to factual errors: Due to its extensive knowledge, it may occasionally provide incorrect facts. |
| Megatron-Turing NLG (NVIDIA) | 530 | - Multilinguality and translation: Effective at handling multiple languages and translations. | - Focus on factual language: May prioritize factual accuracy over creativity. |
| Llama (Meta) | 137 | - Efficiency and code generation: Efficiently generates code and technical content. | - Still under development: Ongoing improvements and refinements are needed. |
| LLaMA 7B (Google AI) | 7 | - Efficiency and balanced capabilities: Efficient while maintaining a good balance of features. | - Less powerful than some other LLMs: May not match the capabilities of larger models. |
| Mistral-7B (Salesforce) | 7 | - Efficiency and balanced capabilities: Efficient with a well-rounded feature set. | - Less powerful than some other LLMs: May not excel in certain specialized tasks. |
| Claude (Anthropic) | Unknown | - Safety focus and alignment with human values: Prioritizes safety and ethical outputs. | - Limited information available: Details about its architecture and performance are scarce. |
- Introduction to Deep Learning with PyTorch: Fundamentals of deep learning and constructing neural networks.
- Intermediate Deep Learning: Essential architectures for images and sequential data.
- Deep Learning for Text with PyTorch: Natural language processing and understanding.
- Introduction to LLMs in Python: Understanding transformer-based models and implementation using libraries like Hugging Face's Transformers.
- Project: Analyzing Car Reviews with LLMs: Apply language modeling skills to real-world tasks.
- Code Along: Fine-Tuning Your Own LLaMA 2 Model: Optimize model performance and deploy in production environments.
- LLMOps Essentials: Best practices for deploying and managing large language models.
This project is licensed under the MIT License.Embrace the future of language models in Python and unleash the full potential of unstructured data.
Feel free to fork, star, and contribute to this repository 🌟🤖
