Update README.md (#3225)

Feels better to put integrations section after the features

Author: jerryzh168

README.md

@@ -103,22 +103,6 @@ pip install torchao
 
 Please see the [torchao compability table](https://github.com/pytorch/ao/issues/2919) for version requirements for dependencies.
 
-## 🔗 Integrations
-
-TorchAO is integrated into some of the leading open-source libraries including:
-
-* Unsloth for QAT, blog post coming soon!
-* HuggingFace transformers with a [builtin inference backend](https://huggingface.co/docs/transformers/main/quantization/torchao) and [low bit optimizers](https://github.com/huggingface/transformers/pull/31865)
-* HuggingFace diffusers best practices with `torch.compile` and TorchAO in a standalone repo [diffusers-torchao](https://github.com/huggingface/diffusers/blob/main/docs/source/en/quantization/torchao.md)
-* vLLM for LLM serving: [usage](https://docs.vllm.ai/en/latest/features/quantization/torchao.html), [detailed docs](https://docs.pytorch.org/ao/main/torchao_vllm_integration.html)
-* Integration with [FBGEMM](https://github.com/pytorch/FBGEMM/tree/main/fbgemm_gpu/experimental/gen_ai) for SOTA kernels on server GPUs
-* Integration with [ExecuTorch](https://github.com/pytorch/executorch/) for edge device deployment
-* Axolotl for [QAT](https://docs.axolotl.ai/docs/qat.html) and [PTQ](https://docs.axolotl.ai/docs/quantize.html)
-* TorchTitan for [float8 pre-training](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md)
-* HuggingFace PEFT for LoRA using TorchAO as their [quantization backend](https://huggingface.co/docs/peft/en/developer_guides/quantization#torchao-pytorch-architecture-optimization)
-* TorchTune for our NF4 [QLoRA](https://docs.pytorch.org/torchtune/main/tutorials/qlora_finetune.html), [QAT](https://docs.pytorch.org/torchtune/main/recipes/qat_distributed.html), and [float8 quantized fine-tuning](https://github.com/pytorch/torchtune/pull/2546) recipes
-* SGLang for LLM serving: [usage](https://docs.sglang.ai/advanced_features/quantization.html#online-quantization)
-
 ## 🔎 Inference
 
 TorchAO delivers substantial performance gains with minimal code changes:
@@ -265,6 +249,21 @@ We've added support for authoring and releasing [custom ops](./torchao/csrc/) th
 If you believe there's other CUDA kernels we should be taking a closer look at please leave a comment on [this issue](https://github.com/pytorch/ao/issues/697) or feel free to contribute directly to the repo.
 -->
 
+## 🔗 Integrations
+
+TorchAO is integrated into some of the leading open-source libraries including:
+
+* Unsloth for QAT, blog post coming soon!
+* HuggingFace transformers with a [builtin inference backend](https://huggingface.co/docs/transformers/main/quantization/torchao) and [low bit optimizers](https://github.com/huggingface/transformers/pull/31865)
+* HuggingFace diffusers best practices with `torch.compile` and TorchAO in a standalone repo [diffusers-torchao](https://github.com/huggingface/diffusers/blob/main/docs/source/en/quantization/torchao.md)
+* vLLM for LLM serving: [usage](https://docs.vllm.ai/en/latest/features/quantization/torchao.html), [detailed docs](https://docs.pytorch.org/ao/main/torchao_vllm_integration.html)
+* Integration with [FBGEMM](https://github.com/pytorch/FBGEMM/tree/main/fbgemm_gpu/experimental/gen_ai) for SOTA kernels on server GPUs
+* Integration with [ExecuTorch](https://github.com/pytorch/executorch/) for edge device deployment
+* Axolotl for [QAT](https://docs.axolotl.ai/docs/qat.html) and [PTQ](https://docs.axolotl.ai/docs/quantize.html)
+* TorchTitan for [float8 pre-training](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md)
+* HuggingFace PEFT for LoRA using TorchAO as their [quantization backend](https://huggingface.co/docs/peft/en/developer_guides/quantization#torchao-pytorch-architecture-optimization)
+* TorchTune for our NF4 [QLoRA](https://docs.pytorch.org/torchtune/main/tutorials/qlora_finetune.html), [QAT](https://docs.pytorch.org/torchtune/main/recipes/qat_distributed.html), and [float8 quantized fine-tuning](https://github.com/pytorch/torchtune/pull/2546) recipes
+* SGLang for LLM serving: [usage](https://docs.sglang.ai/advanced_features/quantization.html#online-quantization)
 
 ## 🎥 Videos
 * [Keynote talk at GPU MODE IRL](https://youtu.be/FH5wiwOyPX4?si=VZK22hHz25GRzBG1&t=1009)