fix: log merge command once done

.gitignore

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

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

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/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/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/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")