---
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

Teaches Qwen Latin using 1.47M texts from FineWeb-2, streamed directly from the Hub.

**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)