Learning Hugging Face LLMs

My hands-on journey learning Large Language Models with the Hugging Face ecosystem. This repository documents real experiments, working code, and practical lessons learned while exploring LLM fine-tuning and deployment.

What's Here

🚀 Working Examples

• test_llama.py - Interactive text generation with Meta-Llama-3-8B-Instruct
• fine_tune_sst2.ipynb - Complete BERT fine-tuning pipeline for sentiment analysis (Trainer API approach)
• fine_tune_mrpc.ipynb - Manual training loop implementation for paraphrase detection (MRPC dataset)
• End-to-end workflows from data loading to model testing

📚 Learning Materials

• fine_tuning_notes.md - Comparison of popular fine-tuning frameworks (SFTTrainer, Unsloth, Axolotl, TorchTune)
• Real examples of parameter tuning and precision management
• Troubleshooting guides for common training issues

🛠 Practical Solutions

• git_troubleshooting_summary.txt - Git workflows for handling large model files
• Device management patterns using device_map="auto" and torch_dtype configurations
• Interactive prompting examples following project conventions

Key Learning Areas

Model Usage & Inference

• Loading models with Hugging Face model hub identifiers
• Text generation pipelines with customizable parameters (top_k, temperature, max_length)
• Device-aware model deployment for CPU/GPU environments

Fine-Tuning Workflows

• Dataset handling with datasets library (SST-2, MRPC, GLUE tasks)
• Training approaches - Trainer API vs manual PyTorch training loops
• Training configuration with TrainingArguments and Trainer API
• Manual training control - Custom loops with DataLoader, optimizer, and device management
• Precision management - balancing performance and stability with BF16/FP16
• Evaluation metrics and model checkpoint management

Development Patterns

• Interactive scripts that prompt for user input rather than hardcoded examples
• Parameter experimentation with exposed configuration options
• Efficient workflows using dynamic padding and batched processing

Repository Structure

Learning-HF-LLMS/
├── test_llama.py              # Text generation example
├── fine_tune_sst2.ipynb       # BERT sentiment classification tutorial (Trainer API)
├── fine_tune_mrpc.ipynb       # BERT paraphrase detection (Manual training loop)
├── fine_tuning_notes.md       # Framework comparison and tips
├── git_troubleshooting_summary.txt  # Git workflow solutions
└── .github/
    └── copilot-instructions.md # Project conventions and patterns

Getting Started

1. Text Generation: Run test_llama.py for interactive LLM experimentation
2. Fine-Tuning (Trainer API): Follow fine_tune_sst2.ipynb for a complete training pipeline with built-in features
3. Fine-Tuning (Manual Loop): Explore fine_tune_mrpc.ipynb to understand low-level PyTorch training mechanics
4. Framework Selection: Check fine_tuning_notes.md for tool recommendations

Integration Stack

• 🤗 Transformers - Core library for model loading and training
• 📊 Datasets - Data loading and preprocessing
• ⚡ PEFT - Parameter-efficient fine-tuning (LoRA/QLoRA)
• 🔧 PyTorch - Backend training framework

Lessons Learned

• Two training approaches: Trainer API for production convenience vs manual loops for learning PyTorch fundamentals
• Dataset variety: Single sentences (SST-2) vs sentence pairs (MRPC) require different tokenization approaches
• Precision matters: Mixed precision conflicts can break training - use consistent dtype configurations
• Device management: device_map="auto" handles multi-GPU setups automatically vs manual .to(device) control
• Git hygiene: Always exclude model folders in .gitignore before committing
• Interactive development: Scripts work better when they prompt for user input

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This is a practical learning repository focused on working code and real solutions rather than theoretical best practices.