See axolotl config

axolotl version: 0.11.0.dev0

adapter: lora
base_model: unsloth/gemma-2-9b-it
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 3ddcdf9e-6fa1-4457-a022-205287c5dbe4_train_data.json
  ds_type: json
  format: custom
  path: /workspace/axolotl/data
  type:
    field_input: input
    field_instruction: instruct
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: null
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /workspace/axolotl/data/3ddcdf9e-6fa1-4457-a022-205287c5dbe4_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_torch_fused
output_dir: /app/checkpoints/3ddcdf9e-6fa1-4457-a022-205287c5dbe4/tournament-tourn_21c0fa9e21db603d_20250808-cac3ab06-07bc-4f2f-98b9-9efb8a49cc1b-5EeL4R63
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 300
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: offline
wandb_name: 3ddcdf9e-6fa1-4457-a022-205287c5dbe4_tournament-tourn_21c0fa9e21db603d_20250808-cac3ab06-07bc-4f2f-98b9-9efb8a49cc1b-5EeL4R63
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3ddcdf9e-6fa1-4457-a022-205287c5dbe4_tournament-tourn_21c0fa9e21db603d_20250808-cac3ab06-07bc-4f2f-98b9-9efb8a49cc1b-5EeL4R63
warmup_steps: 150
weight_decay: 0
xformers_attention: null

app/checkpoints/3ddcdf9e-6fa1-4457-a022-205287c5dbe4/tournament-tourn_21c0fa9e21db603d_20250808-cac3ab06-07bc-4f2f-98b9-9efb8a49cc1b-5EeL4R63

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: 0.0001
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 4
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 150
• training_steps: 1500

Training results

Framework versions

• PEFT 0.15.2
• Transformers 4.53.1
• Pytorch 2.5.1+cu124
• Datasets 3.6.0
• Tokenizers 0.21.2