fix: gemma3_text model loading vision config (#3354)

* fix: gemma3-text mode loading vision config

* fix: improve defaults to use lora kernels

examples/gemma3/gemma-3-1b-qlora.yml

@@ -1,6 +1,7 @@
 base_model: google/gemma-3-1b-it
 
 model_type: Gemma3ForCausalLM
+cls_model_config: Gemma3TextConfig
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
@@ -29,7 +30,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0.05
+lora_dropout: 0
 lora_target_linear: true
 
 sequence_len: 2048

examples/gemma3/gemma-3-270m-qlora.yml

@@ -1,6 +1,7 @@
 base_model: google/gemma-3-270m-it
 
 model_type: Gemma3ForCausalLM
+cls_model_config: Gemma3TextConfig
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
@@ -29,7 +30,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0.05
+lora_dropout: 0
 lora_target_linear: true
 
 sequence_len: 2048

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

@@ -2,6 +2,7 @@ base_model: google/gemma-3-4b-it
 
 # Need to set else transformers tries to load vision too
 model_type: Gemma3ForCausalLM
+cls_model_config: Gemma3TextConfig
 
 load_in_4bit: true
 
@@ -32,8 +33,8 @@ sample_packing: true
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0.05
+lora_dropout: 0
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_linear: true
 
 wandb_project:
 wandb_entity:

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

@@ -31,7 +31,7 @@ pad_to_sequence_len: false
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0.05
+lora_dropout: 0
 lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:

src/axolotl/loaders/utils.py

@@ -5,6 +5,7 @@ from typing import Type
 
 import addict
 import torch
+import transformers
 from transformers import AutoConfig, PretrainedConfig, PreTrainedModel
 
 from axolotl.utils.dict import DictDefault
@@ -153,6 +154,9 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
     This function determines the appropriate model config source, loads it, applies any
     necessary overrides, and validates it for compatibility with the `axolotl` config.
 
+    If `cfg.cls_model_config` is set, a custom config class from transformers will be
+    used instead of `AutoConfig` (e.g., 'LlamaConfig', 'MistralConfig').
+
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
 
@@ -174,8 +178,13 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
     if cfg.num_labels:
         # num_labels is used to initialize classifier models
         config_kwargs["num_labels"] = cfg.num_labels
+
+    config_cls = AutoConfig
+    if cfg.cls_model_config:
+        config_cls = getattr(transformers, cfg.cls_model_config)
+
     try:
-        model_config = AutoConfig.from_pretrained(
+        model_config = config_cls.from_pretrained(
             model_config_name,
             trust_remote_code=trust_remote_code,
             **config_kwargs,

src/axolotl/utils/schemas/model.py

@@ -25,7 +25,12 @@ class ModelInputConfig(BaseModel):
             "description": "If the base_model repo on hf hub doesn't include configuration .json files, You can set that here, or leave this empty to default to base_model"
         },
     )
-    cls_model_config: str | None = None
+    cls_model_config: str | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "transformers config class (e.g., 'LlamaConfig', 'MistralConfig'). Defaults to AutoConfig."
+        },
+    )
     tokenizer_config: str | None = Field(
         default=None,
         json_schema_extra={