README.md

---
license: cc-by-sa-3.0
language:
- ar
- ru
- fr
- es
- zh
- en
size_categories:
- 1M<n<10M
dataset_info:
- config_name: default
  features:
  - name: id
    dtype: string
  - name: url
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: pretrain
    num_bytes: 3895403456
    num_examples: 1200000
  download_size: 2293060098
  dataset_size: 3895403456
- config_name: instruct_tuning
  features:
  - name: instruct
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
  - name: train
    num_bytes: 11251187014
    num_examples: 6720312
  - name: valid
    num_bytes: 590616379
    num_examples: 353701
  download_size: 6802172962
  dataset_size: 11841803393
configs:
- config_name: default
  data_files:
  - split: pretrain
    path: data/pretrain-*
- config_name: instruct_tuning
  data_files:
  - split: train
    path: instruct_tuning/train-*
  - split: valid
    path: instruct_tuning/valid-*
pretty_name: sparrow
---

<div align="center">
<h1>Sparrow</h1>

麻雀虽小 五脏俱全

 Small as it is, the sparrow has all the vital organs

<image src="https://raw.githubusercontent.com/TerenceLiu98/sparrow/master/.github/sparrow.png" width="300" />
</div>

> **The pretraining dataset is collected from [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia)**

---

The **sparrow** project aims to help beginner to understand the base architecture of a large language model from scratch. Not only the model, but also the optimization methods that are widely use to shorten the training process.

- [ ] tokenizer from scratch & merge tokenizer
- [ ] model modules from scratch & train the stacked model
- [ ] supervised fine-tuning
- [ ] Reward Modelling

## Data Preparation

1. For tokenizer and pretraining process, to simplify the data collection process, we use the data from [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia), ensuring that our training corpus is both rich in content and easily accessible. We use 10%-20% of the data with six official language of United Nation — Arabic, Chinese, English, French, Russian, and Spanish—providing a diverse and representative sample for training our tokenizer.

## Tokenizer

A good tokenizer is vital as it is the first component that converts raw text into a structured format a model can understand. It determines the granularity of tokenization and ensures that important elements—such as special tokens marking the beginning and end of a sentence—are consistently incorporated, directly affecting the model's ability to learn and generate language accurately. In `tokenizer/tokenizer.py`, we provide a `class SparrowTokenizer` to help you understand the how a tokenizer been trained. This script demonstrates the complete pipeline—from preprocessing raw data and creating a training corpus, to training a BPE-based tokenizer with customized post-processing for adding special tokens, and finally, saving the vocabulary and configuration files. You can explore this workflow by running:

```bash
python tokenizer/tokenizer.py --args configs/tokenizers.yaml
```

## Models Artitecture 



## Pretraining Set

```python
from datasets import load_dataset
dataset = load_dataset("TerenceLau/sparrow", split="pretrain")
```