fix: clarify how to use lm_eval plugin (#3404) [skip ci]

* feat(doc): clarify how to use scattermoe

* chore: fix wording

docs/cli.qmd

@@ -210,6 +210,8 @@ axolotl lm-eval config.yml
 Configuration options:
 
 ```yaml
+lm_eval_model: # model to evaluate (local or hf path)
+
 # List of tasks to evaluate
 lm_eval_tasks:
   - arc_challenge
@@ -218,7 +220,7 @@ lm_eval_batch_size: # Batch size for evaluation
 output_dir: # Directory to save evaluation results
 ```
 
-See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
+See [LM Eval Harness integration docs](https://docs.axolotl.ai/docs/custom_integrations.html#language-model-evaluation-harness-lm-eval) for full configuration details.
 
 ### delinearize-llama4
 

src/axolotl/integrations/kernels/README.md

@@ -0,0 +1,44 @@
+# Kernels Integration
+
+MoE (Mixture of Experts) kernels speed up training for MoE layers and reduce VRAM costs. In transformers v5, `batched_mm` and `grouped_mm` were integrated as built-in options via the `experts_implementation` config kwarg:
+
+```python
+class ExpertsInterface(GeneralInterface):
+    _global_mapping = {
+        "batched_mm": batched_mm_experts_forward,
+        "grouped_mm": grouped_mm_experts_forward,
+    }
+```
+
+In our custom integration, we add support for **ScatterMoE**, which is even more efficient and faster than `grouped_mm`.
+
+## Usage
+
+Add the following to your axolotl YAML config:
+
+```yaml
+plugins:
+  - axolotl.integrations.kernels.KernelsPlugin
+
+use_kernels: true
+use_scattermoe: true
+```
+
+**Important:** Setting `experts_implementation` is incompatible with `use_scattermoe`.
+
+## How It Works
+
+The `KernelsPlugin` runs before model loading and:
+
+1. Registers the ScatterMoE kernel from the [`axolotl-ai-co/scattermoe`](https://huggingface.co/axolotl-ai-co/scattermoe) Hub repo.
+2. Patches the model's `SparseMoeBlock` forward method with the optimized ScatterMoE implementation.
+
+This works for any MoE model in transformers that uses a `SparseMoeBlock` class (Mixtral, Qwen2-MoE, OLMoE, etc.).
+
+## Limitations
+
+ScatterMoE uses a softmax -> topk routing, so results may be different for some model arch as baseline (GPT-OSS, GLM_MOE_DSA).
+
+## Note on MegaBlocks
+
+We tested [MegaBlocks](https://huggingface.co/kernels-community/megablocks) but were unable to ensure numerical accuracy, so we did not integrate it. It was also incompatible with many newer model architectures in transformers.

src/axolotl/integrations/lm_eval/README.md

@@ -6,6 +6,12 @@ See https://github.com/EleutherAI/lm-evaluation-harness
 
 ## Usage
 
+There are two ways to use the LM Eval integration:
+
+### 1. Post-Training Evaluation
+
+When training with the plugin enabled, evaluation runs automatically after training completes:
+
 ```yaml
 plugins:
   - axolotl.integrations.lm_eval.LMEvalPlugin
@@ -16,9 +22,50 @@ lm_eval_tasks:
   - arc_easy
 
 lm_eval_batch_size: # Batch size for evaluation
-output_dir: # Directory to save evaluation results
+
+# Directory to save evaluation results.
+# The final model is loaded from this directory
+# unless specified otherwise (see below)
+output_dir:
 ```
 
+Run training as usual:
+```bash
+axolotl train config.yml
+```
+
+### 2. Standalone CLI Evaluation
+
+Evaluate any model directly without training:
+
+```yaml
+lm_eval_model: meta-llama/Llama-2-7b-hf
+
+plugins:
+  - axolotl.integrations.lm_eval.LMEvalPlugin
+
+lm_eval_tasks:
+  - gsm8k
+  - hellaswag
+  - arc_easy
+
+lm_eval_batch_size: 8
+output_dir: ./outputs
+```
+
+Run evaluation:
+```bash
+axolotl lm-eval config.yml
+```
+
+## Model Selection Priority
+
+The model to evaluate is selected in the following priority order:
+
+1. **`lm_eval_model`** - Explicit model path or HuggingFace repo (highest priority)
+2. **`hub_model_id`** - Trained model pushed to HuggingFace Hub
+3. **`output_dir`** - Local checkpoint directory containing trained model weights
+
 ## Citation
 
 ```bib

src/axolotl/integrations/lm_eval/__init__.py

@@ -5,7 +5,7 @@ Module for the Plugin for LM Eval Harness
 import subprocess  # nosec
 
 from axolotl.integrations.base import BasePlugin
-from axolotl.integrations.lm_eval.cli import build_lm_eval_command
+from axolotl.integrations.lm_eval.cli import build_lm_eval_command, get_model_path
 
 from .args import LMEvalArgs as LMEvalArgs
 
@@ -29,7 +29,7 @@ class LMEvalPlugin(BasePlugin):
                 wandb_project=cfg.wandb_project,
                 wandb_entity=cfg.wandb_entity,
                 wandb_name=cfg.wandb_name,
-                model=cfg.lm_eval_model or cfg.hub_model_id,
+                model=get_model_path(cfg),
             ):
                 subprocess.run(  # nosec
                     lm_eval_args,

src/axolotl/integrations/lm_eval/cli.py

@@ -13,6 +13,21 @@ import yaml
 from axolotl.utils.dict import DictDefault
 
 
+def get_model_path(cfg: DictDefault) -> str | None:
+    """
+    Determine which model path to use for evaluation.
+
+    Priority order (highest to lowest):
+    1. lm_eval_model - Explicit model path override
+    2. hub_model_id - Model pushed to HuggingFace Hub
+    3. None - Falls back to output_dir in build_lm_eval_command
+
+    Returns:
+        Model path string or None to use output_dir fallback
+    """
+    return cfg.lm_eval_model or cfg.hub_model_id or None
+
+
 def build_lm_eval_command(
     tasks: list[str],
     bfloat16=True,
@@ -108,7 +123,7 @@ def lm_eval(config: str, cloud: Optional[str] = None):
             wandb_project=cfg.wandb_project,
             wandb_entity=cfg.wandb_entity,
             wandb_name=cfg.wandb_name,
-            model=cfg.lm_eval_model or cfg.hub_model_id,
+            model=get_model_path(cfg),
             revision=cfg.revision,
             apply_chat_template=cfg.apply_chat_template,
             fewshot_as_multiturn=cfg.fewshot_as_multiturn,