See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: princeton-nlp/gemma-2-9b-it-SimPO
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - b122a855bc60610b_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/b122a855bc60610b_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 30
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: Romain-XV/95f62437-4820-45c2-a547-34fac88d3eb6
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: 0.05
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
micro_batch_size: 4
mlflow_experiment_name: /tmp/b122a855bc60610b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7157cbce-ebcf-4ad1-8604-0ad26ed1260f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7157cbce-ebcf-4ad1-8604-0ad26ed1260f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

95f62437-4820-45c2-a547-34fac88d3eb6

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: 1.0660

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: 16
• total_train_batch_size: 64
• 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
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
2.983	0.0013	1	3.1788
1.054	0.0653	50	1.1710
1.1699	0.1307	100	1.1397
1.0907	0.1960	150	1.1262
1.1104	0.2613	200	1.1141
1.0401	0.3266	250	1.1062
1.0968	0.3920	300	1.0985
1.0785	0.4573	350	1.0919
1.0792	0.5226	400	1.0861
0.9954	0.5879	450	1.0807
1.1625	0.6533	500	1.0758
1.1131	0.7186	550	1.0724
1.137	0.7839	600	1.0693
1.1217	0.8493	650	1.0672
1.0933	0.9146	700	1.0663
0.9518	0.9799	750	1.0660

Framework versions

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