LLM

🤖 Large Language Models (LLMs)

Welcome to the Large Language Models section of the AI Engineering Academy! This module provides a comprehensive understanding of LLMs and their practical applications in AI engineering.

📚 Repository Structure

Model/Directory	Description & Contents
Axolotl	Framework for fine-tuning language models
Gemma	Google's latest LLM implementation
- finetune-gemma.ipynb - gemma-sft.py - Gemma_finetuning_notebook.ipynb	Fine-tuning notebooks and scripts
LLama2	Meta's open-source LLM
- generate_response_stream.py - Llama2_finetuning_notebook.ipynb - Llama_2_Fine_Tuning_using_QLora.ipynb	Implementation and fine-tuning guides
Llama3	Upcoming Meta LLM experiments
- Llama3_finetuning_notebook.ipynb	Initial fine-tuning experiments
LlamaFactory	LLM training and deployment framework
LLMArchitecture/ParameterCount	Technical details of model architectures
Mistral-7b	Mistral AI's 7B parameter model
- LLM_evaluation_harness_for_Arc_Easy_and_SST.ipynb - Mistral_Colab_Finetune_ipynb_Colab_Final.ipynb - notebooks_chatml_inference.ipynb - notebooks_DPO_fine_tuning.ipynb - notebooks_SFTTrainer TRL.ipynb - SFT.py	Comprehensive notebooks for evaluation, fine-tuning, and inference
Mixtral	Mixtral's mixture-of-experts model
- Mixtral_fine_tuning.ipynb	Fine-tuning implementation
VLM	Visual Language Models
- Florence2_finetuning_notebook.ipynb - PaliGemma_finetuning_notebook.ipynb	Implementations for vision-language models

🎯 Module Overview

1. LLM Architectures

• Explore implementations of:
  • Llama2 (Meta's open-source model)
  • Mistral-7b (Efficient 7B parameter model)
  • Mixtral (Mixture-of-experts architecture)
  • Gemma (Google's latest contribution)
  • Llama3 (Upcoming experiments)

2. 🛠️ Fine-tuning Techniques

• Implementation strategies
• LoRA (Low-Rank Adaptation) approaches
• Advanced optimization methods

3. 🏗️ Model Architecture Analysis

• Deep dives into model structures
• Parameter counting methodologies
• Scaling considerations

4. 🔧 Specialized Implementations

• Code LLama for programming tasks
• Visual Language Models:
  • Florence2
  • PaliGemma

5. 💻 Practical Applications

• Comprehensive Jupyter notebooks
• Response generation pipelines
• Inference implementation guides

6. 🚀 Advanced Topics

• DPO (Direct Preference Optimization)
• SFT (Supervised Fine-Tuning)
• Evaluation methodologies

🤝 Contributing

We welcome contributions! See our contributing guidelines for more information.

📚 Resources

Each subdirectory contains detailed documentation and implementation guides. Check individual README files for specific instructions.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

---

Made with ❤️ for the AI community