Update VLM docs with verified Qwen3-VL config and performance

README.md

@@ -66,6 +66,106 @@ 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": [<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`](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