feat: add gemma3n multimodal mode
This commit is contained in:
@@ -1,7 +1,5 @@
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base_model: google/gemma-3n-E2B-it
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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@@ -18,8 +16,8 @@ load_in_4bit: true
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# - lm_head
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# - embed_tokens
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# huggingface repo
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# chat_template: gemma3
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chat_template: gemma3n
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eot_tokens:
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- <end_of_turn>
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datasets:
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76
examples/gemma3n/gemma-3n-e2b-vision-qlora.yml
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76
examples/gemma3n/gemma-3n-e2b-vision-qlora.yml
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@@ -0,0 +1,76 @@
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base_model: google/gemma-3n-E2B-it
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processor_type: AutoProcessor
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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cut_cross_entropy: true
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# for use with fft to only train on language model layers
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# unfrozen_parameters:
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# - model.language_model.*
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# - lm_head
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# - embed_tokens
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load_in_4bit: true
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# these 3 lines are needed for now to handle vision chat templates w images
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skip_prepare_dataset: true
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remove_unused_columns: false
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sample_packing: false
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# gemma3 doesn't seem to play nice with ddp
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ddp_find_unused_parameters: true
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chat_template: gemma3n
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eot_tokens:
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- <end_of_turn>
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datasets:
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- path: HuggingFaceH4/llava-instruct-mix-vsft
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type: chat_template
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split: train[:1%]
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field_messages: messages
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.01
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output_dir: ./outputs/out
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adapter: qlora
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lora_model_dir:
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sequence_len: 2048
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pad_to_sequence_len: false
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: true
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fp16:
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tf32: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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logging_steps: 1
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# flash_attention: true # Any attention impl does not work with gemma3n now
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warmup_ratio: 0.1
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evals_per_epoch: 1
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saves_per_epoch: 1
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weight_decay: 0.0
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@@ -2,6 +2,7 @@
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from transformers import (
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Gemma3ForConditionalGeneration,
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Gemma3nForConditionalGeneration,
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Llama4ForConditionalGeneration,
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LlavaForConditionalGeneration,
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Mistral3ForConditionalGeneration,
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@@ -18,4 +19,5 @@ MULTIMODAL_AUTO_MODEL_MAPPING = {
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"qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
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"mistral3": Mistral3ForConditionalGeneration,
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"gemma3": Gemma3ForConditionalGeneration,
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"gemma3n": Gemma3nForConditionalGeneration,
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}
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@@ -264,6 +264,26 @@ class Gemma3ProcessingStrategy(ProcessingStrategy):
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return labels
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class Gemma3nProcessingStrategy(ProcessingStrategy):
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"""Processing Strategy class for Gemma3n"""
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def process_labels(self, input_ids):
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labels = input_ids.clone()
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# Follows https://colab.research.google.com/github/huggingface/huggingface-gemma-recipes/blob/main/notebooks/fine_tune_gemma3n_on_t4.ipynb
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labels[labels == self.processor.tokenizer.pad_token_id] = -100
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if hasattr(self.processor.tokenizer, "image_token_id"):
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labels[labels == self.processor.tokenizer.image_token_id] = -100
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if hasattr(self.processor.tokenizer, "audio_token_id"):
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labels[labels == self.processor.tokenizer.audio_token_id] = -100
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if hasattr(self.processor.tokenizer, "boi_token_id"):
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labels[labels == self.processor.tokenizer.boi_token_id] = -100
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if hasattr(self.processor.tokenizer, "eoi_token_id"):
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labels[labels == self.processor.tokenizer.eoi_token_id] = -100
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return labels
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def get_processing_strategy(
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processor: ProcessorMixin,
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chat_template,
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@@ -279,6 +299,10 @@ def get_processing_strategy(
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return Gemma3ProcessingStrategy(
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processor, chat_template, image_size, image_resize_algorithm
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)
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if chat_template_type == "gemma3n":
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return Gemma3nProcessingStrategy(
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processor, chat_template, image_size, image_resize_algorithm
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)
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if chat_template_type in [
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"llama3_2_vision",
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"llama4",
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49
src/axolotl/utils/chat_templates/templates/gemma3n.jinja
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49
src/axolotl/utils/chat_templates/templates/gemma3n.jinja
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@@ -0,0 +1,49 @@
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{{ bos_token }}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '
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' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '
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' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{{ message['content'] | trim }}
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{%- elif message['content'] is iterable -%}
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{%- for item in message['content'] -%}
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{%- if item['type'] == 'audio' -%}
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{{ '<audio_soft_token>' }}
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{%- elif item['type'] == 'image' -%}
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{{ '<image_soft_token>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>
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' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{'<start_of_turn>model
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'}}
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{%- endif -%}
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@@ -62,6 +62,7 @@ class ChatTemplate(str, Enum):
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llava = "llava"
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qwen2_vl = "qwen2_vl"
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gemma3 = "gemma3"
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gemma3n = "gemma3n"
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command_a = "command_a"
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command_a_tool_use = "command_a_tool_use"
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command_a_rag = "command_a_rag"
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