Merge pull request #281 from kelbrown20/update-readme

[Docs] Updates for SDG README

.spellcheck-en-custom.txt

@@ -4,10 +4,13 @@
 Backport
 backported
 codebase
+configs
 Dataset
 dataset
 datasets
 distractor
+Eval
+eval
 FIXME
 freeform
 ICL
@@ -17,12 +20,15 @@ Langchain's
 LLM
 LLMBlock
 MCQ
+Merlinite
+Mixtral
 MMLU
 Ouput
 Pre
 pre
 Pregenerated
 qna
+quantized
 repo
 sdg
 Splitter

README.md

@@ -1,4 +1,4 @@
-# sdg
+# Synthetic Data Generation (SDG)
 
 ![Lint](https://github.com/instructlab/sdg/actions/workflows/lint.yml/badge.svg?branch=main)
 ![Build](https://github.com/instructlab/sdg/actions/workflows/pypi.yaml/badge.svg?branch=main)
@@ -10,3 +10,69 @@
 ![`e2e-nvidia-l40s-x4.yml` on `main`](https://github.com/instructlab/sdg/actions/workflows/e2e-nvidia-l40s-x4.yml/badge.svg?branch=main)
 
 Python library for Synthetic Data Generation
+
+## Introduction
+
+Synthetic Data Generation (SDG) is a process that creates an artificially generated dataset that mimics real data based on provided examples. SDG uses a YAML file containing question-and-answer pairs as input data.
+
+## Installing the SDG library
+
+Clone the library and navigate to the repo:
+
+```bash
+git clone https://github.com/instructlab/sdg
+cd sdg
+```
+
+Install the library:
+
+```bash
+pip install .
+```
+
+### Using the library
+
+You can import SDG into your Python files with the following items:
+
+```python
+ from instructlab.sdg.generate_data import generate_data
+ from instructlab.sdg.utils import GenerateException
+```
+
+## Pipelines
+
+A pipeline is a series of steps to execute in order to generate data.
+
+There are three default pipelines shipped in SDG: `simple`, `full`, and `eval`. Each pipeline requires specific hardware specifications
+
+### Simple Pipeline
+
+The [simple pipeline](src/instructlab/sdg/pipelines/simple) is designed to be used with [quantized Merlinite](https://huggingface.co/instructlab/merlinite-7b-lab-GGUF) as the teacher model. It enables basic data generation results on low-end consumer grade hardware, such as laptops and desktops with small or no discrete GPUs.
+
+### Full Pipeline
+
+The [full pipeline](src/instructlab/sdg/pipelines/full) is designed to be used with [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) as the the teacher model, but has also been successfully tested with smaller models such as [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) and even some quantized versions of the two teacher models. This is the preferred data generation pipeline on higher end consumer grade hardware and all enterprise hardware.
+
+### Eval Pipeline
+
+The [eval pipeline](src/instructlab/sdg/pipelines/eval) is used to generate [MMLU](https://en.wikipedia.org/wiki/MMLU) benchmark data that can be used to later evaluate a trained model on your knowledge dataset. It does not generate data for use during model training.
+
+### Pipeline architecture
+
+All the pipelines are written in a YAML format and must adhere to a [specific schema](src/instructlab/sdg/pipelines/schema/v1.json).
+
+The pipelines that generate data for model training (simple and full pipelines) expect to have three different pipeline configs - one each for knowledge, grounded skills, and freeform skills. They are expected to exist in files called `knowledge.yaml`, `grounded_skills.yaml`, and `freeform_skills.yaml` respectively. For background on the difference in knowledge, grounded skills, and freeform skills, refer to the [InstructLab Taxonomy repository](https://github.com/instructlab/taxonomy).
+
+## Repository structure
+
+```bash
+|-- src/instructlab/ (1)
+|-- docs/ (2)
+|-- scripts/ (3)
+|-- tests/ (4)
+```
+
+1. Contains the SDG code that interacts with InstructLab.
+2. Contains documentation on various SDG methodologies.
+3. Contains some utility scripts, but not part of any supported API.
+4. Contains all the tests for the SDG repository.