moe kernels init scaffold

docs/moe_backends.md

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+MoE Backends in Axolotl
+
+Axolotl supports selecting a Mixture-of-Experts (MoE) compute backend via an environment variable:
+
+- AXOLOTL_MOE_BACKEND=auto|hf_triton|torch_grouped|naive
+
+Behavior
+- auto (default): prefers PyTorch 2.8+ grouped GEMM, then Hugging Face kernels hub, otherwise naive.
+- hf_triton: uses the Hugging Face kernels hub (kernels-community/triton_kernels) when available.
+- torch_grouped: targets PyTorch 2.8+ grouped GEMM.
+- naive: keeps the reference per-expert loop.
+
+Notes
+- Current implementation wires the backend selector and routes Mixtral MoE through it. The hf_triton path is initially a stub: it uses kernels hub for routing but still falls back to per-expert computation until grouped GEMM is fully integrated.
+- No changes to training scripts are required; Axolotl wraps Transformers Trainer; selection happens inside the model forward.
+
+Example
+AXOLOTL_MOE_BACKEND=hf_triton accelerate launch -m axolotl.cli.train path/to/config.yaml