fix: log merge command once done

.gitignore

@@ -193,3 +193,6 @@ out/
 
 # scm auto-versioning
 src/axolotl/_version.py
+
+# macOS
+.DS_Store

docs/cli.qmd

@@ -210,8 +210,6 @@ 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
@@ -220,7 +218,7 @@ lm_eval_batch_size: # Batch size for evaluation
 output_dir: # Directory to save evaluation results
 ```
 
-See [LM Eval Harness integration docs](https://docs.axolotl.ai/docs/custom_integrations.html#language-model-evaluation-harness-lm-eval) for full configuration details.
+See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
 
 ### delinearize-llama4
 

examples/gemma3/gemma-3-4b-qlora.yml

@@ -1,8 +1,7 @@
 base_model: google/gemma-3-4b-it
 
-# Need to set else transformers tries to load vision too
-model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
+plugins:
+  - axolotl.integrations.gemma3.Gemma3TextFromMultimodalPlugin
 
 load_in_4bit: true
 
@@ -30,7 +29,6 @@ lora_model_dir:
 sequence_len: 2048
 sample_packing: true
 
-
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0

examples/qat_nvfp4/Gemma3-12B_baseline.yml

@@ -1,12 +1,11 @@
 base_model: google/gemma-3-12b-it
-# Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-
 load_in_8bit: false
 load_in_4bit: false
 strict: false
 
 plugins:
+  - axolotl.integrations.gemma3.Gemma3TextFromMultimodalPlugin
   - axolotl.integrations.liger.LigerPlugin
 
 liger_rope: true

examples/qat_nvfp4/Gemma3-12B_qat.yml

@@ -7,6 +7,7 @@ load_in_4bit: false
 strict: false
 
 plugins:
+  - axolotl.integrations.gemma3.Gemma3TextFromMultimodalPlugin
   - axolotl.integrations.liger.LigerPlugin
 
 liger_rope: true

examples/qat_nvfp4/Math-Gemma3-12B_baseline.yml

@@ -1,12 +1,11 @@
 base_model: google/gemma-3-12b-it
-# Math finetuning configuration for Gemma3-12B
 # hub_model_id: username/custom_model_name
-
 load_in_8bit: false
 load_in_4bit: false
 strict: false
 
 plugins:
+  - axolotl.integrations.gemma3.Gemma3TextFromMultimodalPlugin
   - axolotl.integrations.liger.LigerPlugin
 
 liger_rope: true

examples/qat_nvfp4/Math-Gemma3-12B_qat.yml

@@ -7,6 +7,7 @@ load_in_4bit: false
 strict: false
 
 plugins:
+  - axolotl.integrations.gemma3.Gemma3TextFromMultimodalPlugin
   - axolotl.integrations.liger.LigerPlugin
 
 liger_rope: true

examples/qat_nvfp4/Math-Gemma3-27B_baseline.yml

@@ -1,12 +1,11 @@
 base_model: google/gemma-3-27b-it
-# Math finetuning configuration for Gemma3-27B
 # hub_model_id: username/custom_model_name
-
 load_in_8bit: false
 load_in_4bit: false
 strict: false
 
 plugins:
+  - axolotl.integrations.gemma3.Gemma3TextFromMultimodalPlugin
   - axolotl.integrations.liger.LigerPlugin
 
 liger_rope: true

examples/qat_nvfp4/Math-Gemma3-27B_qat.yml

@@ -7,6 +7,7 @@ load_in_4bit: false
 strict: false
 
 plugins:
+  - axolotl.integrations.gemma3.Gemma3TextFromMultimodalPlugin
   - axolotl.integrations.liger.LigerPlugin
 
 liger_rope: true

requirements.txt

@@ -2,21 +2,21 @@
 
 # START section of dependencies that don't install on Darwin/MacOS
 bitsandbytes==0.49.1
-triton>=3.4.0
+triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-liger-kernel==0.7.0
+liger-kernel==0.6.4
 # END section
 
 packaging==26.0
 huggingface_hub>=1.1.7
 peft>=0.18.1
 tokenizers>=0.22.1
-transformers==5.2.0
+transformers==5.0.0
 accelerate==1.12.0
 datasets==4.5.0
 deepspeed>=0.18.3
-trl==0.28.0
+trl==0.27.1
 hf_xet==1.2.0
 kernels==0.11.5
 
@@ -63,7 +63,7 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.16.0
+torchao==0.13.0
 openenv-core==0.1.0
 schedulefree==1.4.1
 

scripts/merge_gemma3_multimodal_weights.py

@@ -0,0 +1,225 @@
+"""Merge trained text-only Gemma3 weights back into a full multimodal checkpoint.
+
+After training with the Gemma3TextFromMultimodalPlugin, the saved checkpoint
+contains only the language model weights (with ``model.language_model.*``
+prefix, reversed by transformers v5's key_mapping on save).
+
+This script reconstructs a full ``Gemma3ForConditionalGeneration`` checkpoint by
+combining the trained language model weights with the original vision tower and
+projector weights from the base multimodal model.
+
+Usage::
+
+    python scripts/merge_gemma3_multimodal_weights.py \\
+        --original-model google/gemma-3-4b-it \\
+        --trained-model /path/to/trained/output \\
+        --output-dir /path/to/merged
+"""
+
+import argparse
+import json
+import logging
+from pathlib import Path
+
+import torch
+from huggingface_hub import split_torch_state_dict_into_shards
+from safetensors.torch import load_file, save_file
+from transformers import AutoConfig
+
+LOG = logging.getLogger(__name__)
+
+
+def collect_safetensors(model_dir: Path) -> dict[str, torch.Tensor]:
+    """Load and merge all safetensors shard files in a directory."""
+    shard_files = sorted(model_dir.glob("*.safetensors"))
+    if not shard_files:
+        raise FileNotFoundError(f"No safetensors files found in {model_dir}")
+
+    state_dict: dict[str, torch.Tensor] = {}
+    for shard in shard_files:
+        LOG.info("Loading %s", shard.name)
+        state_dict.update(load_file(str(shard)))
+    return state_dict
+
+
+def merge(
+    original_model: str,
+    trained_model: str,
+    output_dir: str,
+    *,
+    trust_remote_code: bool = False,
+) -> None:
+    original_path = Path(original_model)
+    trained_path = Path(trained_model)
+    out_path = Path(output_dir)
+    out_path.mkdir(parents=True, exist_ok=True)
+
+    # 1. Load the original multimodal checkpoint
+    LOG.info("Loading original multimodal weights from %s", original_model)
+    if original_path.is_dir():
+        original_sd = collect_safetensors(original_path)
+    else:
+        from huggingface_hub import snapshot_download
+
+        cached = Path(
+            snapshot_download(original_model, allow_patterns=["*.safetensors"])
+        )
+        original_sd = collect_safetensors(cached)
+
+    # 2. Load trained text-only weights (already reversed to model.language_model.* by
+    #    transformers v5 key_mapping on save)
+    LOG.info("Loading trained text-only weights from %s", trained_model)
+    trained_sd = collect_safetensors(trained_path)
+
+    # 3. Classify original keys
+    lang_keys = {k for k in original_sd if k.startswith("model.language_model.")}
+    vision_keys = {k for k in original_sd if k.startswith("model.vision_tower.")}
+    projector_keys = {
+        k for k in original_sd if k.startswith("model.multi_modal_projector.")
+    }
+    other_keys = set(original_sd.keys()) - lang_keys - vision_keys - projector_keys
+
+    LOG.info(
+        "Original checkpoint: %d language, %d vision, %d projector, %d other keys",
+        len(lang_keys),
+        len(vision_keys),
+        len(projector_keys),
+        len(other_keys),
+    )
+
+    # 4. Classify trained keys (reverse mapping on save gives model.language_model.* prefix)
+    trained_lang_keys = {k for k in trained_sd if k.startswith("model.language_model.")}
+    trained_other = set(trained_sd.keys()) - trained_lang_keys
+
+    LOG.info(
+        "Trained checkpoint: %d language keys, %d other keys",
+        len(trained_lang_keys),
+        len(trained_other),
+    )
+
+    # 5. Build merged state dict
+    merged: dict[str, torch.Tensor] = {}
+
+    # Keep vision tower and projector from original
+    for key in vision_keys | projector_keys:
+        merged[key] = original_sd[key]
+
+    # Use trained language model weights (overwrite original)
+    for key in trained_lang_keys:
+        merged[key] = trained_sd[key]
+
+    # For other trained keys (like lm_head.weight), use trained version
+    for key in trained_other:
+        merged[key] = trained_sd[key]
+
+    # For any original other keys not covered by trained (shouldn't usually happen),
+    # keep original
+    for key in other_keys:
+        if key not in merged:
+            merged[key] = original_sd[key]
+
+    # Check for missing language keys that were in original but not in trained
+    missing_lang = lang_keys - trained_lang_keys
+    if missing_lang:
+        LOG.warning(
+            "%d language keys in original but not in trained; keeping original: %s",
+            len(missing_lang),
+            list(missing_lang)[:5],
+        )
+        for key in missing_lang:
+            merged[key] = original_sd[key]
+
+    LOG.info("Merged checkpoint: %d total keys", len(merged))
+
+    # 6. Save merged weights (sharded at 50GB, matching transformers default)
+    LOG.info("Saving merged weights to %s", out_path)
+    state_dict_split = split_torch_state_dict_into_shards(merged, max_shard_size="50GB")
+
+    for filename, tensors in state_dict_split.filename_to_tensors.items():
+        shard = {name: merged[name] for name in tensors}
+        save_file(shard, str(out_path / filename))
+
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": {
+                "total_size": sum(t.numel() * t.element_size() for t in merged.values())
+            },
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        with open(out_path / "model.safetensors.index.json", "w") as f:
+            json.dump(index, f, indent=2)
+        LOG.info("Saved %d shards", len(state_dict_split.filename_to_tensors))
+
+    # 7. Copy/update config
+    LOG.info("Writing config.json")
+    original_config = AutoConfig.from_pretrained(
+        original_model, trust_remote_code=trust_remote_code
+    )
+
+    # Update text_config fields from trained model's config if available
+    trained_config_path = trained_path / "config.json"
+    if trained_config_path.exists():
+        with open(trained_config_path) as f:
+            trained_config_dict = json.load(f)
+
+        # The trained config is the text sub-config; merge its fields into
+        # the original composite config's text_config
+        if hasattr(original_config, "text_config"):
+            for key, val in trained_config_dict.items():
+                if key not in ("model_type", "_name_or_path", "architectures"):
+                    if hasattr(original_config.text_config, key):
+                        setattr(original_config.text_config, key, val)
+
+    original_config.save_pretrained(out_path)
+
+    # 8. Copy tokenizer files from trained model if present
+    tokenizer_files = list(trained_path.glob("tokenizer*")) + list(
+        trained_path.glob("special_tokens_map*")
+    )
+    if tokenizer_files:
+        import shutil
+
+        for tok_file in tokenizer_files:
+            shutil.copy2(tok_file, out_path / tok_file.name)
+        LOG.info("Copied %d tokenizer files", len(tokenizer_files))
+
+    LOG.info("Merge complete. Output saved to %s", out_path)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Merge trained text-only Gemma3 weights back into a multimodal checkpoint."
+    )
+    parser.add_argument(
+        "--original-model",
+        required=True,
+        help="HuggingFace model ID or local path to the original multimodal model",
+    )
+    parser.add_argument(
+        "--trained-model",
+        required=True,
+        help="Local path to the trained text-only model output directory",
+    )
+    parser.add_argument(
+        "--output-dir",
+        required=True,
+        help="Directory to save the merged multimodal checkpoint",
+    )
+    parser.add_argument(
+        "--trust-remote-code",
+        action="store_true",
+        default=False,
+        help="Trust remote code when loading model config",
+    )
+    args = parser.parse_args()
+
+    merge(
+        original_model=args.original_model,
+        trained_model=args.trained_model,
+        output_dir=args.output_dir,
+        trust_remote_code=args.trust_remote_code,
+    )
+
+
+if __name__ == "__main__":
+    main()

src/axolotl/core/builders/causal.py

@@ -246,8 +246,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ddp_find_unused_parameters
         )
 
-        if self.cfg.group_by_length:
-            training_arguments_kwargs["train_sampling_strategy"] = "group_by_length"
+        training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)

src/axolotl/core/builders/rl.py

@@ -11,6 +11,7 @@ from axolotl.core.trainers import (
 )
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
+from axolotl.core.trainers.grpo import GRPOStrategy
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import ensure_dtype
 from axolotl.utils.callbacks.qat import QATCallback
@@ -52,8 +53,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         trainer_cls_args = [self.model]
 
         if self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
-            from axolotl.core.trainers.grpo import GRPOStrategy
-
             trainer_cls = GRPOStrategy.get_trainer_class(
                 sequence_parallel=self.cfg.context_parallel_size > 1
             )
@@ -134,17 +133,21 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-            blocklist_args_kwargs.append("max_prompt_length")
+            # Handle when max_prompt_length == max_length from defaults
+            # CPOTrainer requires strictly less than
+            if (
+                training_args_kwargs["max_prompt_length"]
+                == training_args_kwargs["max_length"]
+            ):
+                training_args_kwargs["max_prompt_length"] -= 1
 
         elif self.cfg.rl is RLType.ORPO:
             training_args_cls = AxolotlORPOConfig
 
-            blocklist_args_kwargs.append("max_prompt_length")
-
         elif self.cfg.rl is RLType.KTO:
             training_args_cls = AxolotlKTOConfig
             # KTOConfig in TRL >= 0.27.0 no longer accepts max_prompt_length
-            blocklist_args_kwargs.append("max_prompt_length")
+            blocklist_args_kwargs = ["max_prompt_length"]
 
             training_args_kwargs["desirable_weight"] = (
                 self.cfg.kto_desirable_weight or 1.0
@@ -154,8 +157,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             )
 
         elif self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
-            from axolotl.core.trainers.grpo import GRPOStrategy
-
             training_args_cls = GRPOStrategy.get_training_args_class()
             training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
             blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()

src/axolotl/core/trainers/dpo/trainer.py

@@ -57,18 +57,16 @@ class AxolotlDPOTrainer(
     def tokenize_row(
         features,
         processing_class,
-        max_prompt_length: int | None = None,
-        max_completion_length: int | None = None,
-        add_special_tokens: bool = True,
-        is_chat: bool = False,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
     ) -> Dict:
         res = DPOTrainer.tokenize_row(
             features,
             processing_class,
-            max_prompt_length=max_prompt_length,
-            max_completion_length=max_completion_length,
-            add_special_tokens=add_special_tokens,
-            is_chat=is_chat,
+            max_prompt_length,
+            max_completion_length,
+            add_special_tokens,
         )
         # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
         if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -104,7 +104,7 @@ class CutCrossEntropyPlugin(BasePlugin):
 
     def patch_llama_like(
         self,
-        model_type_to_patch: str,
+        model_type: str,
     ) -> None:
         """
         Generic patch for model architectures with causal lm similar to llama
@@ -112,10 +112,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         from cut_cross_entropy.transformers.patch import PATCH_FNS
 
         def patch_generic(
-            maybe_model,
-            patch_options,
-            remote_model_id: str | None,
-            model_type: str,
+            maybe_model, patch_options, model_type: str, remote_model_id: str | None
         ):
             import cut_cross_entropy.transformers.llama
             from cut_cross_entropy.transformers.llama import cce_forward
@@ -139,13 +136,11 @@ class CutCrossEntropyPlugin(BasePlugin):
                     f"Error: {str(e)}"
                 ) from e
 
-        if model_type_to_patch not in PATCH_FNS:
+        if model_type not in PATCH_FNS:
             LOG.warning_once(
-                "Setting up generic cce patch for model type: %s", model_type_to_patch
+                "Setting up generic cce patch for model type: %s", model_type
             )
             LOG.warning_once(
-                f"Generic Cut Cross Entropy + {model_type_to_patch} support is experimental and may not work as expected."
-            )
-            PATCH_FNS[model_type_to_patch] = partial(
-                patch_generic, model_type=model_type_to_patch
+                f"Generic Cut Cross Entropy + {model_type} support is experimental and may not work as expected."
             )
+            PATCH_FNS[model_type] = partial(patch_generic, model_type=model_type)

src/axolotl/integrations/gemma3/README.md

@@ -0,0 +1,37 @@
+# Gemma3 Text-from-Multimodal Plugin
+
+Load a Gemma3 multimodal checkpoint (e.g. `google/gemma-3-4b-it`) directly into `Gemma3ForCausalLM` for text-only training. This bypasses the multimodal trainer path and enables sample packing and other text-specific optimizations.
+
+## How it works
+
+The plugin uses transformers v5's `key_mapping` parameter on `from_pretrained` to remap `model.language_model.*` checkpoint keys to `model.*`, matching what `Gemma3ForCausalLM` expects. Vision tower and projector weights are automatically discarded. On save, transformers reverses the mapping so checkpoints retain the original `model.language_model.*` prefix.
+
+## Usage
+
+Add the plugin to your YAML config:
+
+```yaml
+base_model: google/gemma-3-4b-it
+
+plugins:
+  - axolotl.integrations.gemma3.Gemma3TextFromMultimodalPlugin
+```
+
+See `examples/gemma3/gemma-3-4b-qlora.yml` for a complete example.
+
+## Merging weights back into a multimodal checkpoint
+
+After training, the saved checkpoint contains only the language model weights. To reconstruct a full `Gemma3ForConditionalGeneration` checkpoint (with the original vision tower and projector), use the merge script:
+
+```bash
+python scripts/merge_gemma3_multimodal_weights.py \
+    --original-model google/gemma-3-4b-it \
+    --trained-model /path/to/trained/output \
+    --output-dir /path/to/merged
+```
+
+This combines:
+- **Trained language model weights** from your output checkpoint
+- **Original vision tower + projector weights** from the base multimodal model
+
+The merged checkpoint can be loaded as `Gemma3ForConditionalGeneration` for multimodal inference or further training.

src/axolotl/integrations/gemma3/__init__.py

@@ -0,0 +1,9 @@
+"""Gemma3 integration for loading multimodal checkpoints as text-only models."""
+
+from .args import Gemma3TextFromMultimodalArgs
+from .plugin import Gemma3TextFromMultimodalPlugin
+
+__all__ = [
+    "Gemma3TextFromMultimodalArgs",
+    "Gemma3TextFromMultimodalPlugin",
+]

src/axolotl/integrations/gemma3/args.py

@@ -0,0 +1,31 @@
+"""Pydantic input args for the Gemma3 text-from-multimodal plugin."""
+
+from pydantic import BaseModel, model_validator
+
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+
+class Gemma3TextFromMultimodalArgs(BaseModel):
+    """Configuration args for loading a Gemma3 multimodal checkpoint as text-only."""
+
+    gemma3_text_from_multimodal: bool = True
+    extract_text_config: bool = False
+
+    @model_validator(mode="before")
+    @classmethod
+    def set_model_type(cls, data):
+        if not isinstance(data, dict):
+            return data
+
+        if not data.get("gemma3_text_from_multimodal", True):
+            return data
+
+        if not data.get("model_type"):
+            LOG.info(
+                "Gemma3TextFromMultimodalPlugin: auto-setting model_type to Gemma3ForCausalLM"
+            )
+            data["model_type"] = "Gemma3ForCausalLM"
+
+        return data

src/axolotl/integrations/gemma3/plugin.py

@@ -0,0 +1,107 @@
+"""Plugin for loading Gemma3 multimodal checkpoints into Gemma3ForCausalLM (text-only).
+
+Uses transformers v5's ``key_mapping`` parameter on ``from_pretrained`` to remap
+``model.language_model.*`` keys to ``model.*``, discarding vision tower and projector
+weights.  On save, transformers automatically reverses the mapping so saved
+checkpoints retain the original ``model.language_model.*`` prefix.
+"""
+
+from axolotl.integrations.base import BasePlugin
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+# key_mapping for transformers from_pretrained:
+# Remap checkpoint keys matching ^model.language_model -> model
+# Vision tower / projector keys won't match any model parameter and are discarded.
+GEMMA3_KEY_MAPPING = {"^model.language_model": "model"}
+
+
+class Gemma3TextFromMultimodalPlugin(BasePlugin):
+    """Load a Gemma3 multimodal checkpoint as a text-only Gemma3ForCausalLM.
+
+    Hooks
+    -----
+    register(cfg)
+        Runs before config validation.  Sets the ``_extract_text_config`` flag,
+        ensures ``model_type`` is ``Gemma3ForCausalLM``, and injects
+        ``key_mapping`` into ``model_kwargs`` so that ``from_pretrained`` remaps
+        ``model.language_model.*`` → ``model.*``.
+
+    pre_model_load(cfg)
+        Runs after config validation/normalization but before model instantiation.
+        Validates that ``model_config_type`` is ``gemma3_text`` and
+        ``is_multimodal`` is False (confirming that ``_extract_text_config``
+        worked correctly).
+    """
+
+    def get_input_args(self) -> str:
+        return "axolotl.integrations.gemma3.Gemma3TextFromMultimodalArgs"
+
+    def register(self, cfg: dict):
+        """Set up config for multimodal → text-only loading.
+
+        This runs before Pydantic validation, so ``cfg`` is a raw dict.
+        """
+        if not cfg.get("gemma3_text_from_multimodal", True):
+            raise ValueError(
+                "Gemma3TextFromMultimodalPlugin: disabled via config, but plugin selected"
+            )
+
+        # Flag for load_model_config() to extract the text sub-config
+        cfg["extract_text_config"] = True
+
+        # Ensure model_type is set for the text-only model class
+        if not cfg.get("model_type"):
+            cfg["model_type"] = "Gemma3ForCausalLM"
+
+        # Inject key_mapping into model_kwargs so from_pretrained remaps weights
+        model_kwargs = cfg.setdefault("model_kwargs", {})
+        model_kwargs["key_mapping"] = GEMMA3_KEY_MAPPING
+
+    def pre_model_load(self, cfg):
+        """Validate that config extraction worked before model instantiation."""
+        if not getattr(cfg, "gemma3_text_from_multimodal", True):
+            return
+
+        if cfg.model_config_type != "gemma3_text":
+            LOG.warning(
+                "Gemma3TextFromMultimodalPlugin: expected model_config_type='gemma3_text' "
+                "but got '%s'. The text config extraction may not have worked.",
+                cfg.model_config_type,
+            )
+
+        if cfg.is_multimodal or cfg.processor_type:
+            raise ValueError(
+                "Multimodal mode is enabled (processor_type set), but "
+                "Gemma3TextFromMultimodalPlugin enabled. "
+                "Please disable one of the two."
+            )
+
+    def post_train(self, cfg, model):
+        """Log merge command after training completes."""
+        if cfg.adapter:
+            LOG.info(
+                "Adapter training detected. To reconstruct the multimodal checkpoint:\n"
+                "  1. Merge adapter weights into the text-only base model:\n"
+                "       axolotl merge_lora <your_config.yml>\n"
+                "  2. Then merge the resulting full model back into the multimodal checkpoint:\n"
+                "       python scripts/merge_gemma3_multimodal_weights.py \\\n"
+                "         --original-model %s \\\n"
+                "         --trained-model %s/merged \\\n"
+                "         --output-dir %s/multi-modal/merged",
+                cfg.base_model,
+                cfg.output_dir,
+                cfg.output_dir,
+            )
+        else:
+            LOG.info(
+                "To merge trained weights back into the multimodal checkpoint, run:\n"
+                "  python scripts/merge_gemma3_multimodal_weights.py \\\n"
+                "    --original-model %s \\\n"
+                "    --trained-model %s \\\n"
+                "    --output-dir %s/multi-modal/merged",
+                cfg.base_model,
+                cfg.output_dir,
+                cfg.output_dir,
+            )

src/axolotl/integrations/kernels/README.md

@@ -1,44 +0,0 @@
-# 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

@@ -6,12 +6,6 @@ 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
@@ -22,50 +16,9 @@ lm_eval_tasks:
   - arc_easy
 
 lm_eval_batch_size: # Batch size for evaluation
-
-# Directory to save evaluation results.
-# The final model is loaded from this directory
-# unless specified otherwise (see below)
-output_dir:
+output_dir: # Directory to save evaluation results
 ```
 
-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, get_model_path
+from axolotl.integrations.lm_eval.cli import build_lm_eval_command
 
 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=get_model_path(cfg),
+                model=cfg.lm_eval_model or cfg.hub_model_id,
             ):
                 subprocess.run(  # nosec
                     lm_eval_args,

src/axolotl/integrations/lm_eval/cli.py

@@ -13,21 +13,6 @@ 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,
@@ -123,7 +108,7 @@ 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=get_model_path(cfg),
+            model=cfg.lm_eval_model or cfg.hub_model_id,
             revision=cfg.revision,
             apply_chat_template=cfg.apply_chat_template,
             fewshot_as_multiturn=cfg.fewshot_as_multiturn,

src/axolotl/loaders/patch_manager.py

@@ -10,7 +10,6 @@ from functools import cached_property
 import addict
 import transformers
 from transformers import PretrainedConfig, PreTrainedModel
-from transformers.modeling_flash_attention_utils import is_flash_attn_available
 
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import (
@@ -501,7 +500,6 @@ class PatchManager:
             and not self.cfg.trust_remote_code
             and not self.cfg.gptq
             and self.cfg.flash_attention
-            and is_flash_attn_available()
             and not self.inference
         ):
             # TODO(MengqingCao): split these patches separately

src/axolotl/loaders/utils.py

@@ -204,6 +204,13 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
 
     check_model_config(cfg, model_config)
 
+    # Extract text config from composite config when explicitly requested
+    # (set by plugins like Gemma3TextFromMultimodalPlugin)
+    if getattr(cfg, "extract_text_config", False) and hasattr(
+        model_config, "get_text_config"
+    ):
+        model_config = model_config.get_text_config()
+
     return model_config
 
 

src/axolotl/monkeypatch/gradient_checkpointing/offload_cpu.py

@@ -59,12 +59,7 @@ class CPU_Offloaded_Gradient_Checkpointer(torch.autograd.Function):
         hidden_states = hidden_states.to("cuda", non_blocking=True).detach()
         hidden_states.requires_grad = True
         with torch.enable_grad():
-            output = ctx.forward_function(hidden_states, *ctx.args)
-            # Newer HF models (e.g. Qwen3MoE) using GradientCheckpointingLayer
-            # return a plain tensor, not a tuple.  Older models return tuples
-            # like (hidden_states, present_kv, ...).  Unwrap if needed.
-            if isinstance(output, (tuple, list)):
-                (output,) = output
+            (output,) = ctx.forward_function(hidden_states, *ctx.args)
         torch.autograd.backward(output, dY)
         return (
             None,

src/axolotl/monkeypatch/transformers/trainer_loss_calc.py

@@ -28,12 +28,8 @@ PATCHED_EVAL_CODE = {
     "array": 'metrics[f"{metric_key_prefix}_loss"] = np.nanmean(all_losses).item()',
 }
 
-ORIGINAL_MAYBE_CODE = (
-    "tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).mean().item()"
-)
-PATCHED_MAYBE_CODE = (
-    "tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).nanmean().item()"
-)
+ORIGINAL_MAYBE_CODE = "tr_loss_scalar = self._nested_gather(tr_loss).mean().item()"
+PATCHED_MAYBE_CODE = "tr_loss_scalar = self._nested_gather(tr_loss).nanmean().item()"
 
 
 def check_evaluation_loop_is_patchable() -> bool:

src/axolotl/utils/schemas/config.py

@@ -446,16 +446,7 @@ class AxolotlInputConfig(
         },
     )
 
-    unfrozen_parameters: list[str] | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "List of regex patterns for parameter names to keep unfrozen. "
-            "All other parameters will be frozen via requires_grad=False. "
-            "Note: range-based patterns (e.g. embed_tokens.weight$[:32000]) use gradient "
-            "zeroing rather than a true freeze, so weight decay will still apply to the "
-            "frozen portion and optimizer states are allocated for the full parameter."
-        },
-    )
+    unfrozen_parameters: list[str] | None = None
 
     sequence_len: int = Field(
         default=512,

src/axolotl/utils/trainer.py

@@ -247,7 +247,7 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2, raise_on_drop=F
 
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    drop_attn_mask = cfg.model_config_type in ["mamba", "gemma3"]
+    drop_attn_mask = cfg.model_config_type in ["mamba", "gemma3", "gemma3_text"]
     if drop_attn_mask:
         LOG.info("dropping attention_mask column")
         train_dataset = train_dataset.remove_columns("attention_mask")

tests/core/test_builders.py

@@ -300,6 +300,7 @@ class TestHFRLTrainerBuilder:
         self._test_common_training_arguments(training_arguments, rl=orpo_cfg.rl)
         # ORPO specific
         assert training_arguments.beta == 0.1  # maps from orpo_alpha
+        assert training_arguments.max_prompt_length == 512
 
     def test_kto_training_arguments(self, kto_cfg, model, tokenizer):
         builder = HFRLTrainerBuilder(kto_cfg, model, tokenizer)

tests/e2e/multigpu/test_fsdp1.py

@@ -186,7 +186,6 @@ class TestFSDP1:
 
         verify_training_success(temp_dir)
 
-    @pytest.mark.skip(reason="slow test, deprecate fsdp1 asap")
     def test_dpo_fft(self, temp_dir):
         cfg = DictDefault(
             {

tests/e2e/multigpu/test_fsdp2.py

@@ -365,7 +365,6 @@ class TestFSDP2:
 
         verify_training_success(temp_dir)
 
-    @pytest.mark.skip(reason="slow test w cu129 + torch 2.9.1 + py3.12")
     @require_torch_2_7_0
     def test_dpo_fft(self, temp_dir):
         cfg = DictDefault(