Update Transformers/readme.md

README.md

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-license: mit
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+---
+license: mit
+---
+![image.png](https://cdn-uploads.huggingface.co/production/uploads/68205a944ab07873c714ab38/JaZj4EZaOlQyoUlkhXO9o.png)  
+ ⚡ Hugging Face Accelerate – Effortless Multi-GPU & Distributed Training
+---
+Welcome to **🤗 Accelerate**, your lightweight, zero-boilerplate training tool for **PyTorch** and **Transformers**. Whether you're working on a single laptop, multi-GPU setup, or a large-scale cloud cluster, Accelerate abstracts away the complexity of hardware and distributed systems — so you can focus on building great models.
+
+---
+
+## ✨ Why Use Accelerate?
+
+- **Single-to-Multi GPU/TPU** in *one line*.
+- **No code refactor** – use the same script everywhere.
+- **Launch-ready**: Easily scale training jobs from dev to prod.
+- **Supports DeepSpeed, FSDP, TPU, Multi-node**.
+- **Compatible with 🤗 Transformers, Datasets, PEFT**, and more.
+- **Built-in CLI** for quick configuration and debugging.
+
+> Accelerate is perfect for DevOps, MLOps, and Full Stack AI teams looking to scale training workloads without managing deep infrastructure internals.
+
+---
+
+## ⚙️ Installation
+
+```bash
+pip install accelerate
+```
+
+Optional: For DeepSpeed, TPU, FSDP, and other accelerators:
+
+```bash
+pip install "accelerate[deepspeed]"
+pip install "accelerate[torch_xla]"
+pip install "accelerate[fsdp]"
+```
+
+---
+
+## 🚀 Quick Start
+
+### Step 1: Configure
+
+```bash
+accelerate config
+```
+
+You'll be guided through an interactive setup (or use `accelerate config default` to auto-generate).
+
+### Step 2: Launch your script
+
+```bash
+accelerate launch train.py
+```
+
+Accelerate automatically applies device mapping, DDP/FSDP strategy, gradient accumulation, and more — based on your config.
+
+---
+
+## 🧪 Example: Training a Transformer at Scale
+
+**train.py**
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
+from datasets import load_dataset
+from accelerate import Accelerator
+
+accelerator = Accelerator()
+dataset = load_dataset("imdb")
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+def tokenize(batch):
+    return tokenizer(batch["text"], padding=True, truncation=True)
+
+tokenized = dataset.map(tokenize, batched=True)
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
+
+training_args = TrainingArguments(
+    output_dir="output",
+    per_device_train_batch_size=8,
+    evaluation_strategy="epoch",
+    num_train_epochs=1,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized["train"].shuffle(seed=42).select(range(2000)),
+    eval_dataset=tokenized["test"].select(range(500)),
+)
+
+trainer.train()
+```
+
+Then just run:
+
+```bash
+accelerate launch train.py
+```
+
+---
+
+## ⚙️ Advanced Use Cases
+
+Accelerate supports:
+
+- **DeepSpeed**: ZeRO offloading, memory savings
+- **FSDP**: Fine-grained model sharding
+- **TPUs**: Train on TPU cores seamlessly
+- **Multi-node/multi-GPU**: via SLURM or CLI
+
+Configure all options interactively or manually edit the `~/.cache/huggingface/accelerate/default_config.yaml`.
+
+---
+
+## 🧰 API Highlights
+
+- `Accelerator()`: Auto-handles devices, mixed precision, gradient clipping, logging.
+- `.prepare()`: Wraps model, dataloader, optimizer for distributed training.
+- `.print()`: Replace all `print()` calls for synchronized logging.
+- `.wait_for_everyone()`: Barrier sync in multi-process setups.
+
+Example:
+
+```python
+accelerator = Accelerator()
+model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
+```
+
+---
+
+## 🧠 DevOps / MLOps Friendly
+
+- **CI/CD ready**: Use CLI and scripting without touching training code.
+- **Built-in logging**: Compatible with 🤗 Hub, WandB, TensorBoard.
+- **Cloud scaling**: Easily used with SageMaker, Vertex AI, GCP, Azure.
+- **Kubernetes compatible**: Launch jobs with config-driven strategy.
+
+---
+
+## 🧩 Integrates With
+
+- 🤗 Transformers
+- 🤗 Datasets
+- 🤗 PEFT (for LoRA / adapters)
+- 🤗 Diffusers
+- DeepSpeed / FSDP / Torch XLA
+- PyTorch Lightning (via wrapper)
+
+---
+
+## 📚 Learn More
+
+- **Docs**: [https://huggingface.co/docs/accelerate](https://huggingface.co/docs/accelerate)
+- **Course**: [Chapter 8 – Distributed Training](https://huggingface.co/course/chapter8)
+- **Blog**: [How to Train BERT with Accelerate](https://huggingface.co/blog)
+
+---
+
+## 🤝 Contribute
+
+```bash
+git clone https://github.com/huggingface/accelerate
+cd accelerate
+pip install -e ".[dev]"
+```
+
+Check issues, help improve features, or share examples for TPU/FSx setups!
+
+
+## License
+
+Accelerate is released under the Apache 2.0 License.
+
+> *Accelerate bridges the gap between single-device experimentation and full-scale model training — with zero boilerplate and maximum flexibility.*
+
+---
+
+Made with love by [Hugging Face](https://huggingface.co) and the open-source community.