See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: princeton-nlp/gemma-2-9b-it-SimPO
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 0f581be30ac3d477_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/0f581be30ac3d477_train_data.json
  type:
    field_instruction: text
    field_output: annotation
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: dzanbek/f7f218d2-31c1-4c45-9224-136e0ecf258e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 50
micro_batch_size: 4
mlflow_experiment_name: /tmp/0f581be30ac3d477_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f7f218d2-31c1-4c45-9224-136e0ecf258e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f7f218d2-31c1-4c45-9224-136e0ecf258e
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

f7f218d2-31c1-4c45-9224-136e0ecf258e

This model is a fine-tuned version of princeton-nlp/gemma-2-9b-it-SimPO on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5334

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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 10
• training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
15.639	0.0006	1	15.4848
11.6	0.0028	5	9.3170
2.848	0.0055	10	1.5961
0.7817	0.0083	15	0.7636
0.6277	0.0110	20	0.6655
0.6471	0.0138	25	0.6167
0.4796	0.0166	30	0.5794
0.4897	0.0193	35	0.5564
0.5998	0.0221	40	0.5419
0.5932	0.0248	45	0.5364
0.415	0.0276	50	0.5334

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.5.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1