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docs/source/en/kernels.md

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+<!---Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+
+# Kernels
+
+Custom kernels target specific ops like matrix multiplications, attention, and normalization to run them faster. Fusing multiple ops into a single kernel reduces memory bandwidth usage by reading and writing GPU memory fewer times, and cuts per-op launch overhead.
+
+## Hub kernels
+
+The [Hub](https://huggingface.co/kernels-community) hosts community kernels you can load with [`KernelConfig`]. Pass the config to `kernel_config` in [`~AutoModelForCausalLM.from_pretrained`]. Once the kernel is loaded, it's active for training. Read the [Loading kernels](./kernel_doc/loading_kernels#kernelconfig) guide for all available options.
+
+```py
+from transformers import AutoModelForCausalLM, KernelConfig
+
+kernel_config = KernelConfig(
+    kernel_mapping={
+        "RMSNorm": "kernels-community/rmsnorm",
+    }
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-0.6B",
+    use_kernels=True,
+    kernel_config=kernel_config,
+)
+```
+
+## Liger
+
+[Liger Kernel](https://github.com/linkedin/Liger-Kernel) fuses layers like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy into single Triton kernels. It's compatible with FlashAttention, FSDP, and DeepSpeed, and improves multi-GPU training throughput while reducing memory usage, making larger vocabularies, batch sizes, and context lengths more feasible.
+
+```bash
+pip install liger-kernel
+```
+
+Set `use_liger_kernel=True` in [`TrainingArguments`] to patch the corresponding model layers with Liger's kernels.
+
+> [!TIP]
+> See the [patching](https://github.com/linkedin/Liger-Kernel#patching) page for a complete list of supported models.
+
+```py
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(
+    ...,
+    use_liger_kernel=True
+)
+```
+
+To control which layers are patched, pass `liger_kernel_config` as a dict. Available options vary by model and include: `rope`, `swiglu`, `cross_entropy`, `fused_linear_cross_entropy`, `rms_norm`, etc.
+
+```py
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(
+    ...,
+    use_liger_kernel=True,
+    liger_kernel_config={
+        "rope": True,
+        "cross_entropy": True,
+        "rms_norm": False,
+        "swiglu": True,
+    }
+)
+```
+
+## Next steps
+
+- See the [Attention backends](./attention_interface) guide for details on kernels like FlashAttention that reduce memory usage.
+- See the [torch.compile](./torch_compile) guide to learn how to compile the forward and backward pass for your entire training step.