> For the complete documentation index, see [llms.txt](https://training.continuumlabs.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://training.continuumlabs.ai/data.md).

# Data

- [Datasets](https://training.continuumlabs.ai/data/datasets.md)
- [Pre Training Data](https://training.continuumlabs.ai/data/datasets/pre-training-data.md): Training Foundation Models
- [Types of Fine Tuning](https://training.continuumlabs.ai/data/datasets/types-of-fine-tuning.md): Some definitions
- [Self Instruct Paper](https://training.continuumlabs.ai/data/datasets/self-instruct-paper.md): The most highly cited paper on fine tuning methods
- [Self-Alignment with Instruction Backtranslation](https://training.continuumlabs.ai/data/datasets/self-alignment-with-instruction-backtranslation.md): Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer, Jason Weston, Mike Lewis
- [Systematic Evaluation of Instruction-Tuned Large Language Models on Open Datasets](https://training.continuumlabs.ai/data/datasets/systematic-evaluation-of-instruction-tuned-large-language-models-on-open-datasets.md)
- [Instruction Tuning](https://training.continuumlabs.ai/data/datasets/instruction-tuning.md): Inspired by the Self-Instruct Paper
- [Instruction Fine Tuning - Alpagasus](https://training.continuumlabs.ai/data/datasets/instruction-fine-tuning-alpagasus.md): "ALPAGASUS: Data-Driven Data Selection for Instruction Fine-Tuning"
- [Less is More For Alignment](https://training.continuumlabs.ai/data/datasets/less-is-more-for-alignment.md): Co-authored by researchers from Meta, Carnegie Mellon University, University of Southern California, and Tel Aviv University
- [Enhanced Supervised Fine Tuning](https://training.continuumlabs.ai/data/datasets/enhanced-supervised-fine-tuning.md): How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
- [Visualising Data using t-SNE](https://training.continuumlabs.ai/data/datasets/visualising-data-using-t-sne.md)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://training.continuumlabs.ai/data/datasets/umap-uniform-manifold-approximation-and-projection-for-dimension-reduction.md)
- [Training and Evaluation Datasets](https://training.continuumlabs.ai/data/datasets/training-and-evaluation-datasets.md)
- [What is perplexity?](https://training.continuumlabs.ai/data/datasets/what-is-perplexity.md)