Embark on an exciting journey to master data science, where you’ll gain the power to fine-tune, restructure, quantize, and retrain local LLMs like Ollama. This ambitious yet incredibly rewarding quest blends traditional data science, cutting-edge machine learning, and specialized deep learning for large language models.
Foundational Data Science Skills:
- Python Programming:
- Core Python (data structures, control flow, functions, OOP).
- File I/O.
- Virtual environments and package management (
pip,conda).
- Data Manipulation and Analysis:
- NumPy: Efficient array operations, linear algebra.
- Pandas: Data loading, cleaning, transformation, and analysis with DataFrames.
- Data Visualization: Matplotlib, Seaborn (for understanding data distributions, model performance).
- Machine Learning Fundamentals (Traditional ML):
- Scikit-learn: Supervised learning (regression, classification), unsupervised learning (clustering), model evaluation metrics, cross-validation.
- Feature engineering.
- Understanding bias-variance tradeoff, overfitting, underfitting.
Deep Learning and LLM-Specific Skills:
- Deep Learning Frameworks:
- PyTorch (highly recommended) or TensorFlow: Tensor operations, defining neural network architectures, training loops, optimizers, loss functions, GPU acceleration.
- Natural Language Processing (NLP) Fundamentals:
- Text preprocessing (tokenization, stemming, lemmatization).
- Word embeddings (Word2Vec, GloVe, FastText - conceptual understanding).
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs) - conceptual.
- Attention Mechanisms and Transformers: This is critical for LLMs. Understanding how they work is fundamental.
- Large Language Model (LLM) Architectures:
- Decoder-only models (GPT-series): Causal language modeling.
- Encoder-decoder models (T5, BART): Sequence-to-sequence tasks.
- Understanding model sizes (parameters: 7B, 13B, 70B etc.).
- Open-source LLM families (Llama, Mistral, Gemma, Qwen, Phi).
- LLM Pre-training and Fine-tuning Concepts:
- Pre-training: Conceptual understanding of how base models are trained on vast text data.
- Fine-tuning: Customizing LLMs for specific tasks or domains.
- Supervised Fine-tuning (SFT): Training on labeled datasets (question-answer pairs, instruction-following).
- Instruction Fine-tuning: Aligning models to follow instructions.
- Parameter-Efficient Fine-Tuning (PEFT): LoRA, QLoRA (understanding how they work to reduce computational resources for fine-tuning).
- Reinforcement Learning from Human Feedback (RLHF) / Direct Preference Optimization (DPO): Aligning models with human preferences (conceptual understanding for advanced work).
- Data Preparation for Fine-tuning:
- Data collection and curation.
- Data cleaning, labeling, and structuring (e.g., into chat templates like ChatML).
- Synthetic data generation.
- LLM Quantization: Making Models Lean for Local Deployment:
- Reducing model size and memory footprint (e.g., 4-bit, 8-bit quantization) to run on local/edge devices.
- LLM Deployment and Serving (Local):
- Ollama: How to use Ollama to download, serve, and manage local LLMs.
- Converting fine-tuned models to formats compatible with local inference (e.g., GGUF).
- Hardware considerations for local LLMs (GPU VRAM, RAM).
- Agentic AI Frameworks (for Application Building):
- LangChain / LangGraph: Building intelligent agents, chaining LLM calls, integrating tools, managing memory, and constructing complex workflows.
- CrewAI: For multi-agent systems and collaborative task execution.
- n8n: For workflow automation and integration of LLMs with other services.
- Retrieval-Augmented Generation (RAG):
- Understanding when to use RAG vs. fine-tuning.
- Components of a RAG system: Document loaders, text splitters, embedding models, vector databases (ChromaDB, Pinecone, Weaviate), retrievers.
- Integrating RAG with local LLMs (Ollama + LangChain/LlamaIndex).
- MLOps/LLMOps (Operationalizing LLMs):
- Experiment tracking (e.g., Weights & Biases for fine-tuning).
- Model versioning.
- Monitoring performance and cost.
- Debugging agent behavior (e.g., LangSmith).