feat: add gemma3n multimodal mode

This commit is contained in:
NanoCode012
2025-07-08 12:58:45 +07:00
parent bd8d98d51f
commit f56ed11dad
6 changed files with 154 additions and 4 deletions

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@@ -1,7 +1,5 @@
base_model: google/gemma-3n-E2B-it
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
@@ -18,8 +16,8 @@ load_in_4bit: true
# - lm_head
# - embed_tokens
# huggingface repo
# chat_template: gemma3
chat_template: gemma3n
eot_tokens:
- <end_of_turn>
datasets:

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@@ -0,0 +1,76 @@
base_model: google/gemma-3n-E2B-it
processor_type: AutoProcessor
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
cut_cross_entropy: true
# for use with fft to only train on language model layers
# unfrozen_parameters:
# - model.language_model.*
# - lm_head
# - embed_tokens
load_in_4bit: true
# these 3 lines are needed for now to handle vision chat templates w images
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false
# gemma3 doesn't seem to play nice with ddp
ddp_find_unused_parameters: true
chat_template: gemma3n
eot_tokens:
- <end_of_turn>
datasets:
- path: HuggingFaceH4/llava-instruct-mix-vsft
type: chat_template
split: train[:1%]
field_messages: messages
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/out
adapter: qlora
lora_model_dir:
sequence_len: 2048
pad_to_sequence_len: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: true
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
logging_steps: 1
# flash_attention: true # Any attention impl does not work with gemma3n now
warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0

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@@ -2,6 +2,7 @@
from transformers import (
Gemma3ForConditionalGeneration,
Gemma3nForConditionalGeneration,
Llama4ForConditionalGeneration,
LlavaForConditionalGeneration,
Mistral3ForConditionalGeneration,
@@ -18,4 +19,5 @@ MULTIMODAL_AUTO_MODEL_MAPPING = {
"qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
"mistral3": Mistral3ForConditionalGeneration,
"gemma3": Gemma3ForConditionalGeneration,
"gemma3n": Gemma3nForConditionalGeneration,
}

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@@ -264,6 +264,26 @@ class Gemma3ProcessingStrategy(ProcessingStrategy):
return labels
class Gemma3nProcessingStrategy(ProcessingStrategy):
"""Processing Strategy class for Gemma3n"""
def process_labels(self, input_ids):
labels = input_ids.clone()
# Follows https://colab.research.google.com/github/huggingface/huggingface-gemma-recipes/blob/main/notebooks/fine_tune_gemma3n_on_t4.ipynb
labels[labels == self.processor.tokenizer.pad_token_id] = -100
if hasattr(self.processor.tokenizer, "image_token_id"):
labels[labels == self.processor.tokenizer.image_token_id] = -100
if hasattr(self.processor.tokenizer, "audio_token_id"):
labels[labels == self.processor.tokenizer.audio_token_id] = -100
if hasattr(self.processor.tokenizer, "boi_token_id"):
labels[labels == self.processor.tokenizer.boi_token_id] = -100
if hasattr(self.processor.tokenizer, "eoi_token_id"):
labels[labels == self.processor.tokenizer.eoi_token_id] = -100
return labels
def get_processing_strategy(
processor: ProcessorMixin,
chat_template,
@@ -279,6 +299,10 @@ def get_processing_strategy(
return Gemma3ProcessingStrategy(
processor, chat_template, image_size, image_resize_algorithm
)
if chat_template_type == "gemma3n":
return Gemma3nProcessingStrategy(
processor, chat_template, image_size, image_resize_algorithm
)
if chat_template_type in [
"llama3_2_vision",
"llama4",

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@@ -0,0 +1,49 @@
{{ bos_token }}
{%- if messages[0]['role'] == 'system' -%}
{%- if messages[0]['content'] is string -%}
{%- set first_user_prefix = messages[0]['content'] + '
' -%}
{%- else -%}
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
' -%}
{%- endif -%}
{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set first_user_prefix = "" -%}
{%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
{%- endif -%}
{%- if (message['role'] == 'assistant') -%}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{{ '<start_of_turn>' + role + '
' + (first_user_prefix if loop.first else "") }}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'audio' -%}
{{ '<audio_soft_token>' }}
{%- elif item['type'] == 'image' -%}
{{ '<image_soft_token>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type") }}
{%- endif -%}
{{ '<end_of_turn>
' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{'<start_of_turn>model
'}}
{%- endif -%}

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@@ -62,6 +62,7 @@ class ChatTemplate(str, Enum):
llava = "llava"
qwen2_vl = "qwen2_vl"
gemma3 = "gemma3"
gemma3n = "gemma3n"
command_a = "command_a"
command_a_tool_use = "command_a_tool_use"
command_a_rag = "command_a_rag"