Update README for streaming training script

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

README.md

@@ -3,345 +3,97 @@ viewer: false
 tags:
 - uv-script
 - training
-- vlm
 - unsloth
-- iconclass
+- streaming
 - fine-tuning
+- llm
 ---
 
-# VLM Training with Unsloth
+# Streaming LLM Training with Unsloth
 
-Fine-tune Vision-Language Models efficiently using [Unsloth](https://github.com/unslothai/unsloth) - get 2x faster training with lower memory usage!
+Train on massive datasets without downloading anything - data streams directly from the Hub.
 
-## 🎨 Example: Iconclass VLM
+## 🦥 Latin LLM Example
 
-This directory contains scripts for fine-tuning VLMs to generate [Iconclass](https://iconclass.org) metadata codes from artwork images. Iconclass is a hierarchical classification system used in art history and cultural heritage.
+Teaches Qwen Latin using 1.47M texts from FineWeb-2, streamed directly from the Hub.
 
-### What You'll Train
+**Blog post:** [Train on Massive Datasets Without Downloading](https://danielvanstrien.xyz/posts/2026/hf-streaming-unsloth/train-massive-datasets-without-downloading.html)
 
-Given an artwork image, the model outputs structured JSON:
-
-```json
-{
-  "iconclass-codes": ["25H213", "25H216", "25I"]
-}
-```
-
-Where codes represent:
-- `25H213`: river
-- `25H216`: waterfall
-- `25I`: city-view with man-made constructions
-
-## 🚀 Quick Start
-
-### Option 1: Run on HF Jobs (Recommended)
-
-```bash
-# Set your HF token
-export HF_TOKEN=your_token_here
-
-# Submit training job
-python submit_training_job.py
-```
-
-That's it! Your model will train on cloud GPUs and automatically push to the Hub.
-
-### Option 2: Run Locally (Requires GPU)
+### Quick Start
 
 ```bash
-# Install UV (if not already installed)
-curl -LsSf https://astral.sh/uv/install.sh | sh
-
-# Run training
-uv run iconclass-vlm-sft.py \
-  --base-model Qwen/Qwen3-VL-8B-Instruct \
-  --dataset davanstrien/iconclass-vlm-sft \
-  --output-model your-username/iconclass-vlm
-```
-
-### Option 3: Quick Test (100 steps)
-
-```bash
-uv run iconclass-vlm-sft.py \
-  --base-model Qwen/Qwen3-VL-8B-Instruct \
-  --dataset davanstrien/iconclass-vlm-sft \
-  --output-model your-username/iconclass-vlm-test \
-  --max-steps 100
-```
-
-## 📋 Requirements
-
-### For HF Jobs
-- Hugging Face account with Jobs access
-- HF token with write permissions
-
-### For Local Training
-- CUDA-capable GPU (A100 recommended, A10G works)
-- 40GB+ VRAM for 8B models (with 4-bit quantization)
-- Python 3.11+
-- [UV](https://docs.astral.sh/uv/) installed
-
-## 🎛️ Configuration
+# 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
 
-### Quick Config via Python Script
-
-Edit `submit_training_job.py`:
-
-```python
-# Model and dataset
-BASE_MODEL = "Qwen/Qwen3-VL-8B-Instruct"
-DATASET = "davanstrien/iconclass-vlm-sft"
-OUTPUT_MODEL = "your-username/iconclass-vlm"
-
-# Training settings
-BATCH_SIZE = 2
-GRADIENT_ACCUMULATION = 8
-LEARNING_RATE = 2e-5
-MAX_STEPS = None  # Auto-calculate for 1 epoch
-
-# LoRA settings
-LORA_R = 16
-LORA_ALPHA = 32
-
-# GPU
-GPU_FLAVOR = "a100-large"  # or "a100", "a10g-large"
+# Run locally
+uv run latin-llm-streaming.py \
+  --max-steps 100 \
+  --output-repo your-username/qwen-latin-test
 ```
 
-### Full CLI Options
+### Why Streaming?
 
-```bash
-uv run iconclass-vlm-sft.py --help
-```
+- **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
 
-Key arguments:
+### Options
 
 | Argument | Default | Description |
 |----------|---------|-------------|
-| `--base-model` | Required | Base VLM (e.g., Qwen/Qwen3-VL-8B-Instruct) |
-| `--dataset` | Required | Training dataset on HF Hub |
-| `--output-model` | Required | Where to push your model |
-| `--lora-r` | 16 | LoRA rank (higher = more capacity) |
-| `--lora-alpha` | 32 | LoRA alpha (usually 2×r) |
-| `--learning-rate` | 2e-5 | Learning rate |
-| `--batch-size` | 2 | Per-device batch size |
-| `--gradient-accumulation` | 8 | Gradient accumulation steps |
-| `--max-steps` | Auto | Total training steps |
-| `--num-epochs` | 1.0 | Epochs (if max-steps not set) |
-
-## 🏗️ Architecture
-
-### What Makes This Fast?
-
-1. **Unsloth Optimizations**: 2x faster training through:
-   - Optimized CUDA kernels
-   - Better memory management
-   - Efficient gradient checkpointing
-
-2. **4-bit Quantization**:
-   - Loads model in 4-bit precision
-   - Dramatically reduces VRAM usage
-   - Minimal impact on quality with LoRA
-
-3. **LoRA (Low-Rank Adaptation)**:
-   - Only trains 0.1-1% of parameters
-   - Much faster than full fine-tuning
-   - Easy to merge back or share
-
-### Training Flow
-
-```
-Dataset (HF Hub)
-    ↓
-FastVisionModel.from_pretrained (4-bit)
-    ↓
-Apply LoRA adapters
-    ↓
-SFTTrainer (Unsloth-optimized)
-    ↓
-Push to Hub with model card
-```
-
-## 📊 Expected Performance
-
-### Training Time (Qwen3-VL-8B on A100)
-
-| Dataset Size | Batch Config | Time | Cost (est.) |
-|--------------|--------------|------|-------------|
-| 44K samples | BS=2, GA=8 | ~4h | $16 |
-| 10K samples | BS=2, GA=8 | ~1h | $4 |
-| 1K samples | BS=2, GA=8 | ~10min | $0.70 |
-
-*BS = Batch Size, GA = Gradient Accumulation*
-
-### GPU Requirements
+| `--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 |
 
-| Model Size | Min GPU | Recommended | VRAM Usage |
-|------------|---------|-------------|------------|
-| 3B-4B | A10G | A100 | ~20GB |
-| 7B-8B | A100 | A100 | ~35GB |
-| 13B+ | A100 (80GB) | A100 (80GB) | ~60GB |
+### Performance
 
-## 🔍 Monitoring Your Job
+| Environment | Speed | Why |
+|-------------|-------|-----|
+| Colab A100 | ~0.36 it/s | Network latency |
+| HF Jobs A100 | ~0.74 it/s | Co-located compute |
 
-### Via CLI
+Streaming is ~2x faster on HF Jobs because compute is co-located with the data.
 
-```bash
-# Check status
-hfjobs status your-job-id
-
-# Stream logs
-hfjobs logs your-job-id --follow
-
-# List all jobs
-hfjobs list
-```
-
-### Via Python
-
-```python
-from huggingface_hub import HfApi
-
-api = HfApi()
-job = api.get_job("your-job-id")
-
-print(job.status)
-print(job.logs())
-```
-
-### Via Web
-
-Your job URL: `https://huggingface.co/jobs/your-username/your-job-id`
-
-## 🎯 Using Your Fine-Tuned Model
-
-```python
-from unsloth import FastVisionModel
-from PIL import Image
-
-# Load your model
-model, tokenizer = FastVisionModel.from_pretrained(
-    model_name="your-username/iconclass-vlm",
-    load_in_4bit=True,
-    max_seq_length=2048,
-)
-FastVisionModel.for_inference(model)
-
-# Prepare input
-image = Image.open("artwork.jpg")
-prompt = "Extract ICONCLASS labels for this image."
-
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image"},
-            {"type": "text", "text": prompt},
-        ],
-    }
-]
-
-# Apply chat template
-inputs = tokenizer.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    return_tensors="pt",
-).to("cuda")
-
-# Generate
-outputs = model.generate(
-    **inputs,
-    max_new_tokens=256,
-    temperature=0.7,
-    top_p=0.9,
-)
-
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(response)
-# {"iconclass-codes": ["31A235", "31A24(+1)", "61B(+54)"]}
-```
-
-## 📦 Files in This Directory
-
-| File | Purpose |
-|------|---------|
-| `iconclass-vlm-sft.py` | Main training script (UV script) |
-| `submit_training_job.py` | Helper to submit HF Jobs |
-| `README.md` | This file |
-
-## 🛠️ Troubleshooting
-
-### Out of Memory?
-
-Reduce batch size or increase gradient accumulation:
-```bash
---batch-size 1 --gradient-accumulation 16
-```
-
-### Training Too Slow?
-
-Increase batch size if you have VRAM:
-```bash
---batch-size 4 --gradient-accumulation 4
-```
-
-### Model Not Learning?
+---
 
-Try adjusting learning rate:
-```bash
---learning-rate 5e-5  # Higher
---learning-rate 1e-5  # Lower
-```
+## 🚀 Running on HF Jobs
 
-Or increase LoRA rank:
 ```bash
---lora-r 32 --lora-alpha 64
-```
+# Basic usage
+hf jobs uv run latin-llm-streaming.py --flavor a100-large --secrets HF_TOKEN
 
-### Jobs Failing?
+# With timeout for long training
+hf jobs uv run latin-llm-streaming.py --flavor a100-large --timeout 2h --secrets HF_TOKEN
 
-Check logs:
-```bash
-hfjobs logs your-job-id
+# Pass script arguments after --
+hf jobs uv run latin-llm-streaming.py --flavor a100-large -- --max-steps 1000 --batch-size 8
 ```
 
-Common issues:
-- HF_TOKEN not set correctly
-- Output model repo doesn't exist (create it first)
-- GPU out of memory (reduce batch size)
+### Available Flavors
 
-## 🔗 Related Resources
+- `a100-large` - 80GB VRAM (recommended)
+- `a10g-large` - 24GB VRAM
+- `t4-small` - 16GB VRAM
 
-- **Unsloth**: https://github.com/unslothai/unsloth
-- **Unsloth Docs**: https://docs.unsloth.ai/
-- **TRL**: https://github.com/huggingface/trl
-- **HF Jobs**: https://huggingface.co/docs/hub/spaces-sdks-jobs
-- **UV**: https://docs.astral.sh/uv/
-- **Iconclass**: https://iconclass.org
-- **Blog Post**: https://danielvanstrien.xyz/posts/2025/iconclass-vlm-sft/
-
-## 💡 Tips
-
-1. **Start Small**: Test with `--max-steps 100` before full training
-2. **Use Wandb**: Add `--report-to wandb` for better monitoring
-3. **Save Often**: Use `--save-steps 50` for checkpoints
-4. **Multiple GPUs**: Script automatically uses all available GPUs
-5. **Resume Training**: Load from checkpoint with `--resume-from-checkpoint`
-
-## 📝 Citation
+---
 
-If you use this training setup, please cite:
+## 🔗 Resources
 
-```bibtex
-@misc{iconclass-vlm-training,
-  author = {Daniel van Strien},
-  title = {Efficient VLM Fine-tuning with Unsloth for Art History},
-  year = {2025},
-  publisher = {GitHub},
-  howpublished = {\url{https://github.com/davanstrien/uv-scripts}}
-}
-```
+- [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) •
-Powered by 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
+Made with 🦥 [Unsloth](https://github.com/unslothai/unsloth)