Gemma Qlora Error : 'GemmaConfig' object has no attribute 'mlp_bias'

[hanhee1126]

I want to run qLoRA with Gemma-2B
But I got the error 'GemmaConfig' object has no attribute 'mlp_bias'
How can I fix this?
Thank you for reading

!pip install torch=="2.1.2"
!pip install -e git+https://github.com/OpenAccess-AI-Collective/axolotl#egg=axolotl
!pip install flash-attn=="2.5.0"
!pip install deepspeed=="0.13.1"
!pip install mlflow=="2.13.0"

import yaml

# Your YAML string
yaml_string = """
base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: duarteocarmo/lusiadas
    type: alpaca
hub_model_id: duarteocarmo/lusiaidas
val_set_size: 0.1
output_dir: ./out

adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: lusiaidas
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model: checkpoint


gradient_accumulation_steps: 3
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
"""

# Buy using the ! the comand will be executed as a bash command
!accelerate launch -m axolotl.cli.train /content/test_axolotl.yaml

# Buy using the ! the comand will be executed as a bash command
!accelerate launch -m axolotl.cli.inference /content/test_axolotl.yaml \
    --qlora_model_dir="./qlora-out" --gradio

[hanhee1126]

It was solved with a simple fix.I omitted this step.
write this snippet for someone like me XD
have a good day

yaml_dict = yaml.safe_load(yaml_string)

# Specify your file path
file_path = 'test_axolotl.yaml'

# Write the YAML file
with open(file_path, 'w') as file:
    yaml.dump(yaml_dict, file)