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