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README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- expert-generated
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- fs90/nano-start-data
+task_categories:
+- text-generation
+pretty_name: Nano-Start Tokenized Dataset
+tags:
+- educational
+- llm-training
+- tokenized
+- binary
+- oxidizr
+---
+
+# Nano-Start Tokenized Dataset
+
+Pre-tokenized binary files ready for training with [oxidizr](https://github.com/farhan-syah/oxidizr). This is the tokenized version of [fs90/nano-start-data](https://huggingface.co/datasets/fs90/nano-start-data).
+
+## What is Tokenization?
+
+Language models don't process text directly - they work with numbers called **tokens**. Tokenization converts text into token IDs:
+
+```
+"Hello world" → [9906, 1917]
+```
+
+This dataset is **pre-tokenized** for simplicity - download and start training immediately. To learn how tokenization works and create your own datasets, see the [splintr](https://github.com/farhan-syah/splintr) project.
+
+## Quick Start
+
+**Option A: Using hf**
+```bash
+pip install huggingface_hub
+hf download fs90/nano-start-data-bin --local-dir data/nano-start/tokenized --repo-type dataset
+```
+
+**Option B: Direct download**
+
+Download `combined.bin` from the [Files tab](https://huggingface.co/datasets/fs90/nano-start-data-bin/tree/main) and place it in your project.
+
+**Train with oxidizr:**
+```bash
+cargo run --release -- \
+    --config models/nano-start.yaml \
+    --data data/nano-start/tokenized/combined.bin
+```
+
+## Files
+
+Download `combined.bin` for training - it contains all data merged together:
+
+| File | Size | Tokens | Description |
+|------|------|--------|-------------|
+| **`combined.bin`** | 25,516 bytes | 6,379 | **All data merged (recommended)** |
+
+### Individual Files (Optional)
+
+You can also train on individual subsets. Training on different data produces different model behavior:
+
+| File | Size | Tokens | Description |
+|------|------|--------|-------------|
+| `completions.bin` | 8,788 bytes | 2,197 | Factual statements only |
+| `qa.bin` | 11,036 bytes | 2,759 | Q&A pairs only |
+| `chat.bin` | 5,692 bytes | 1,423 | Multi-turn conversations only |
+
+Experiment with different files to see how the training data affects model behavior!
+
+## Binary Format
+
+Each `.bin` file contains raw token IDs:
+
+- **Encoding**: u32 (32-bit unsigned integer)
+- **Byte order**: Little-endian
+- **Headers**: None (raw token stream)
+- **Tokenizer**: `cl100k_base` (OpenAI, vocab size: 100,331)
+
+### Reading the Data
+
+```python
+import struct
+
+def read_tokens(path):
+    with open(path, "rb") as f:
+        data = f.read()
+    return list(struct.unpack(f"<{len(data)//4}I", data))
+
+tokens = read_tokens("combined.bin")
+print(f"Total tokens: {len(tokens)}")
+```
+
+## Tokenizer Details
+
+| Property | Value |
+|----------|-------|
+| Tokenizer | `cl100k_base` (OpenAI GPT-4/GPT-3.5) |
+| Vocab size | 100,331 |
+| EOS token | `<\|endoftext\|>` (ID: 100257) |
+
+### Special Tokens
+
+| Token | ID | Purpose |
+|-------|------|---------|
+| `<\|endoftext\|>` | 100257 | Separates examples |
+| `<\|system\|>` | 100277 | System instructions |
+| `<\|user\|>` | 100278 | User input |
+| `<\|assistant\|>` | 100279 | Model response |
+
+## Source Data
+
+To see the human-readable text before tokenization: [fs90/nano-start-data](https://huggingface.co/datasets/fs90/nano-start-data)
+
+## Related Resources
+
+- **Raw data**: [fs90/nano-start-data](https://huggingface.co/datasets/fs90/nano-start-data)
+- **Training framework**: [oxidizr](https://github.com/farhan-syah/oxidizr)
+- **Tokenization**: [splintr](https://github.com/farhan-syah/splintr) - Learn how to tokenize your own data
+
+## License
+
+MIT License
+
+## Citation
+
+```bibtex
+@dataset{nano_start_bin_2024,
+  title={Nano-Start Tokenized Dataset},
+  author={fs90},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/fs90/nano-start-data-bin}
+}
+```