math_genie_gemma

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>

<details><summary>See axolotl config</summary>

axolotl version: 0.10.0

base_model: ./gemma
model_type: Gemma3ForCausalLM

model_quantization_config: null
load_in_4bit: false
load_in_8bit: false

# gemma3 doesn't seem to play nice with ddp
# ddp_find_unused_parameters: true

datasets:
  - path: ./dataset/math_genie/
    type:
      system_prompt: ""
      field_system: system
      field_instruction: question
      field_output: answer
      format: "Question:{instruction}\nAnswer:"
      no_input_format: "Question:{instruction}\nAnswer:"
    ds_type: arrow

output_dir: ./outputs/gemma/math_genie

adapter: lora
lora_model_dir:

sequence_len: 1500
sample_packing: true

lora_r: 8
lora_alpha: 16
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: true
fp16:
tf32: true

gradient_checkpointing: false
gradient_checkpointing_kwargs:
  use_reentrant: false
logging_steps: 1
flash_attention: true
eager_attention:

warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

</details><br>

outputs/gemma/math_genie

This model was trained from scratch on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 197
• training_steps: 1970

Training results

Framework versions

• PEFT 0.15.2
• Transformers 4.57.1
• Pytorch 2.9.1+cu128
• Datasets 3.6.0
• Tokenizers 0.22.1