Blog
Builder Center2026We benchmark Task Arithmetic, TIES, DARE, and Model Soups for merging Qwen3.5 experts across five domains. The best expert training duration turns out to depend on the merging method, with sparsification-based methods peaking well past the validation optimum.
AWS Blog2026We paired a fine-tuned Qwen3-32B with Claude Sonnet orchestration over Amazon Bedrock, SageMaker, and MCP to build a multi-model coding assistant. Fine-tuning on 7,000 annotated functions cut C++ architecture compliance violations to 0.02 against generic models.
Self-published2022Fine-tuning a pre-trained network works better when different layers move at different speeds. We set up discriminative learning rates with PyTorch parameter groups, so early layers retain what they learned while later layers adapt faster.