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--- |
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viewer: false |
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tags: |
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- uv-script |
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- training |
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- unsloth |
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- streaming |
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- fine-tuning |
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- llm |
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--- |
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# Streaming LLM Training with Unsloth |
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Train on massive datasets without downloading anything - data streams directly from the Hub. |
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## 🦥 Latin LLM Example |
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Teaches Qwen Latin using 1.47M texts from FineWeb-2, streamed directly from the Hub. |
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**Blog post:** [Train on Massive Datasets Without Downloading](https://danielvanstrien.xyz/posts/2026/hf-streaming-unsloth/train-massive-datasets-without-downloading.html) |
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### Quick Start |
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```bash |
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# Run on HF Jobs (recommended - 2x faster streaming) |
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hf jobs uv run latin-llm-streaming.py \ |
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--flavor a100-large \ |
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--timeout 2h \ |
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--secrets HF_TOKEN \ |
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-- \ |
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--max-steps 500 \ |
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--output-repo your-username/qwen-latin |
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# Run locally |
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uv run latin-llm-streaming.py \ |
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--max-steps 100 \ |
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--output-repo your-username/qwen-latin-test |
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``` |
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### Why Streaming? |
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- **No disk space needed** - train on TB-scale datasets without downloading |
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- **Works everywhere** - Colab, Kaggle, HF Jobs |
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- **Any language** - FineWeb-2 has 90+ languages available |
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### Options |
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| Argument | Default | Description | |
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|----------|---------|-------------| |
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| `--base-model` | `unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit` | Base model | |
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| `--max-steps` | 500 | Training steps | |
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| `--batch-size` | 4 | Per-device batch size | |
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| `--gradient-accumulation` | 4 | Gradient accumulation steps | |
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| `--learning-rate` | 2e-4 | Learning rate | |
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| `--output-repo` | Required | Where to push model | |
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| `--wandb-project` | None | Wandb project for logging | |
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### Performance |
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| Environment | Speed | Why | |
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|-------------|-------|-----| |
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| Colab A100 | ~0.36 it/s | Network latency | |
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| HF Jobs A100 | ~0.74 it/s | Co-located compute | |
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Streaming is ~2x faster on HF Jobs because compute is co-located with the data. |
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--- |
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## 🚀 Running on HF Jobs |
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```bash |
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# Basic usage |
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hf jobs uv run latin-llm-streaming.py --flavor a100-large --secrets HF_TOKEN |
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# With timeout for long training |
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hf jobs uv run latin-llm-streaming.py --flavor a100-large --timeout 2h --secrets HF_TOKEN |
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# Pass script arguments after -- |
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hf jobs uv run latin-llm-streaming.py --flavor a100-large -- --max-steps 1000 --batch-size 8 |
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``` |
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### Available Flavors |
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- `a100-large` - 80GB VRAM (recommended) |
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- `a10g-large` - 24GB VRAM |
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- `t4-small` - 16GB VRAM |
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--- |
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## 🔗 Resources |
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- [Unsloth](https://github.com/unslothai/unsloth) - 2x faster training |
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- [HF Jobs Docs](https://huggingface.co/docs/huggingface_hub/guides/jobs) |
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- [Datasets Streaming](https://huggingface.co/docs/datasets/stream) |
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- [Streaming Datasets Blog](https://huggingface.co/blog/streaming-datasets) |
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--- |
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Made with 🦥 [Unsloth](https://github.com/unslothai/unsloth) |
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