--- 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. --- ## 🎨 VLM Streaming Fine-tuning (Qwen3-VL) Fine-tune Vision Language Models with streaming datasets - ideal for large image-text datasets. **Script:** `vlm-streaming-sft-unsloth-qwen.py` **Default model:** `unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit` **Example dataset:** [`davanstrien/iconclass-vlm-sft`](https://huggingface.co/datasets/davanstrien/iconclass-vlm-sft) > **Note:** This script uses pinned dependencies (`transformers==4.57.1`, `trl==0.22.2`) matching the [official Unsloth Qwen3-VL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(7B)-Vision.ipynb) for maximum compatibility. ### Quick Start ```bash # Run on HF Jobs (recommended) hf jobs uv run \ --flavor a100-large \ --secrets HF_TOKEN \ -- \ https://huggingface.co/datasets/uv-scripts/training/raw/main/vlm-streaming-sft-unsloth-qwen.py \ --max-steps 500 \ --output-repo your-username/vlm-finetuned # With Trackio monitoring dashboard hf jobs uv run \ --flavor a100-large \ --secrets HF_TOKEN \ -- \ https://huggingface.co/datasets/uv-scripts/training/raw/main/vlm-streaming-sft-unsloth-qwen.py \ --max-steps 500 \ --output-repo your-username/vlm-finetuned \ --trackio-space your-username/trackio ``` ### Why Streaming for VLMs? - **No disk space needed** - images stream directly from Hub - **Works with massive datasets** - train on datasets larger than your storage - **Memory efficient** - Unsloth uses ~60% less VRAM - **2x faster** - Unsloth optimizations for Qwen3-VL ### Verified Performance Tested on HF Jobs with A100-80GB: | Setting | Value | |---------|-------| | Model | Qwen3-VL-8B (4-bit) | | Dataset | iconclass-vlm-sft | | Speed | ~3s/step | | 50 steps | ~3 minutes | | Starting loss | 4.3 | | Final loss | ~0.85 | ### Options | Argument | Default | Description | |----------|---------|-------------| | `--base-model` | `unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit` | Base VLM model | | `--dataset` | `davanstrien/iconclass-vlm-sft` | Dataset with images + messages | | `--max-steps` | 500 | Training steps (required for streaming) | | `--batch-size` | 2 | Per-device batch size | | `--gradient-accumulation` | 4 | Gradient accumulation steps | | `--learning-rate` | 2e-4 | Learning rate | | `--lora-r` | 16 | LoRA rank | | `--lora-alpha` | 16 | LoRA alpha (same as r per Unsloth notebook) | | `--output-repo` | Required | Where to push model | | `--trackio-space` | None | HF Space for Trackio dashboard | ### Dataset Format The script works with **any dataset** that has `images` and `messages` columns in the standard VLM conversation format: ```python { "images": [], # Single image or list of images "messages": [ {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Describe this image"}]}, {"role": "assistant", "content": [{"type": "text", "text": "The image shows..."}]} ] } ``` **Compatible datasets:** - [`davanstrien/iconclass-vlm-sft`](https://huggingface.co/datasets/davanstrien/iconclass-vlm-sft) - Art iconography classification - Any dataset following the [Unsloth VLM format](https://docs.unsloth.ai/basics/vision-finetuning) ### Calculating Steps from Dataset Size Since streaming datasets don't expose their length, use this formula: ``` steps = dataset_size / (batch_size * gradient_accumulation) ``` For example, with 10,000 samples, batch_size=2, gradient_accumulation=4: ``` steps = 10000 / (2 * 4) = 1250 steps for 1 epoch ``` --- ## 🚀 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)