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

Quick Start

# 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.

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🎨 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

Note: This script uses pinned dependencies (transformers==4.57.1, trl==0.22.2) matching the official Unsloth Qwen3-VL notebook for maximum compatibility.

Quick Start

# 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:

{
    "images": [<PIL.Image>],  # 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 - Art iconography classification
• Any dataset following the Unsloth VLM format

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

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🚀 Running on HF Jobs

# 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

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🔗 Resources

• Unsloth - 2x faster training
• HF Jobs Docs
• Datasets Streaming
• Streaming Datasets Blog

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Made with 🦥 Unsloth