Developed production-ready support for the Qwen3.5 dense model family within the Liger-Kernel repository, enabling compatibility across models ranging from 0.8B to 27B parameters. The work involved implementing a dedicated entry point for applying Liger kernels to Qwen3.5, patching core components such as RMSNorm, SwiGLU MLP, and fused cross-entropy, and integrating Qwen3_5ForCausalLM. Validation included comprehensive end-to-end testing on NVIDIA H200 hardware, with results demonstrating high logits cosine similarity and minimal training loss differences. All updates were reflected in documentation and test utilities, leveraging deep learning, model optimization, and Python development skills to ensure robust integration.