SWIFT Trainer Integration (#240)

## Summary
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## Details
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## Testing Done
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README.md

@@ -91,7 +91,7 @@ With one line of code, Liger Kernel can increase throughput by more than 20% and
 - **Exact:** Computation is exact—no approximations! Both forward and backward passes are implemented with rigorous unit tests and undergo convergence testing against training runs without Liger Kernel to ensure accuracy.
 - **Lightweight:** Liger Kernel has minimal dependencies, requiring only Torch and Triton—no extra libraries needed! Say goodbye to dependency headaches!
 - **Multi-GPU supported:** Compatible with multi-GPU setups (PyTorch FSDP, DeepSpeed, DDP, etc.).
-- **Trainer Framework Integration**: [Axolotl](https://github.com/axolotl-ai-cloud/axolotl), [LLaMa-Factory](https://github.com/hiyouga/LLaMA-Factory), [SFTTrainer](https://github.com/huggingface/trl/releases/tag/v0.10.1), [Hugging Face Trainer](https://github.com/huggingface/transformers/pull/32860)
+- **Trainer Framework Integration**: [Axolotl](https://github.com/axolotl-ai-cloud/axolotl), [LLaMa-Factory](https://github.com/hiyouga/LLaMA-Factory), [SFTTrainer](https://github.com/huggingface/trl/releases/tag/v0.10.1), [Hugging Face Trainer](https://github.com/huggingface/transformers/pull/32860), [SWIFT](https://github.com/modelscope/ms-swift)
 
 ## Target Audiences