3. Data, Science & AI

Configures and optimizes 16-bit LoRA and rsLoRA for efficient large language model fine-tuning with customizable parameters.

Provides vectorized string operations for data cleaning and search using NumPy's char module.

Provides vectorized operations for NumPy arrays, including reductions, in-place modifications, and custom function wrapping.

Provides strategies for continued pretraining and domain adaptation in Unsloth, leveraging rsLoRA and embedding adjustments.

Handles date and time operations using numpy's datetime64 and timedelta64, including business day offsets for time series data.

Provides core linear algebra operations in NumPy including matrix multiplication, SVD, and least squares fitting for data science and AI applications.

Optimized implementation of Group Relative Policy Optimization (GRPO) for training reasoning models with 8x memory savings.

Builds RAG systems using Ollama with local and cloud models for document Q&A and knowledge bases via LangChain.

Optimizes AI model training with dynamic 4-bit quantization and FP8 to reduce VRAM usage without accuracy loss.

Trains AI models with extended context lengths using optimized RoPE scaling and memory-efficient attention kernels.

Builds and manages LLM agents with LangChain and LangGraph, including tool integration, state handling, and LangSmith observability.

Fetches and summarizes web content (websites, YouTube) using AI for concise insights.

Architects scalable AI agent systems for Microsoft 365 Copilot, enabling seamless integration of AI capabilities across productivity applications.

Exports PyTorch models to ONNX with dynamic axes handling, graph optimization, and INT8 quantization for cross-platform deployment.

Provides efficient file I/O for numpy, including binary (npy/npz), text (CSV), and memory-mapped data handling for large datasets.

Enables supervised fine-tuning (SFT) for language models with SFTTrainer, instruction formatting, and multi-turn dataset preparation using standard templates.

Enables defining and executing tool-calling patterns for LLMs, including schema design, validation, and parallel calls to build and debug function workflows.

Enables exact weight updates and optimized gradient checkpointing for full fine-tuning in Unsloth, triggered by specific keywords.

Offers standard and NaN-robust statistical functions for data analysis, including histograms and correlation matrices.

Enables seamless data interoperability between NumPy and other scientific libraries using DLPack, buffer protocols, and function overriding.

Provides NumPy-based FFT routines for spectral analysis, signal filtering, and frequency-domain operations.

PyTorch library for computer vision tasks featuring pretrained models, advanced image transformations, and data handling utilities for bounding boxes and masks.

Standardizes and formats datasets for Unsloth, including chat template conversion and synthetic data generation for AI training.

Fine-tunes multimodal vision-language models (e.g., Llama 3.2 Vision, Qwen2.5 VL) using optimized vision layers for enhanced performance.