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:
  - 625865432dbb1ed0_train_data.json
  ds_type: json
  field: answer
  path: /workspace/input_data/625865432dbb1ed0_train_data.json
  type: completion
debug: null
deepspeed: null
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: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: leixa/562b395e-82d3-4e7f-bfd6-c91ee5e35a23
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 8
mlflow_experiment_name: /tmp/625865432dbb1ed0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: leixa-personal
wandb_mode: online
wandb_name: 562b395e-82d3-4e7f-bfd6-c91ee5e35a23
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 562b395e-82d3-4e7f-bfd6-c91ee5e35a23
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

562b395e-82d3-4e7f-bfd6-c91ee5e35a23

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: 0.9969

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• 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: 10
• training_steps: 130

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0230	1	9.3099
3.933	0.2529	11	3.1609
2.1133	0.5057	22	2.0404
1.4659	0.7586	33	1.6160
1.234	1.0115	44	1.3426
0.9555	1.2644	55	1.2751
1.0438	1.5172	66	1.2140
0.7003	1.7701	77	1.1194
0.9279	2.0230	88	1.0316
0.5523	2.2759	99	1.0169
0.6258	2.5287	110	1.0043
0.6285	2.7816	121	0.9969

Framework versions

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