See axolotl config

axolotl version: 0.4.1

yaml

1adapter: lora 2auto_resume_from_checkpoints: true 3base_model: bigscience/bloomz-560m 4bf16: auto 5chat_template: llama3 6dataset_prepared_path: null 7dataset_processes: 6 8datasets: 9- data_files: 10 - 31f811fb709cc914_train_data.json 11 ds_type: json 12 format: custom 13 path: /workspace/input_data/31f811fb709cc914_train_data.json 14 type: 15 field_instruction: instruction 16 field_output: response 17 format: '{instruction}' 18 no_input_format: '{instruction}' 19 system_format: '{system}' 20 system_prompt: '' 21debug: null 22deepspeed: null 23early_stopping_patience: 3 24eval_max_new_tokens: 128 25eval_steps: 200 26eval_table_size: null 27evals_per_epoch: null 28flash_attention: false 29fp16: false 30fsdp: null 31fsdp_config: null 32gradient_accumulation_steps: 4 33gradient_checkpointing: true 34group_by_length: false 35hub_model_id: error577/39d93b8a-13bf-40e9-8ba8-8d338e0337b1 36hub_repo: null 37hub_strategy: checkpoint 38hub_token: null 39learning_rate: 0.0002 40load_in_4bit: false 41load_in_8bit: false 42local_rank: null 43logging_steps: 1 44lora_alpha: 16 45lora_dropout: 0.05 46lora_fan_in_fan_out: null 47lora_model_dir: null 48lora_r: 8 49lora_target_linear: true 50lr_scheduler: cosine 51max_grad_norm: 1.0 52max_steps: null 53micro_batch_size: 2 54mlflow_experiment_name: /tmp/31f811fb709cc914_train_data.json 55model_type: AutoModelForCausalLM 56num_epochs: 3 57optimizer: adamw_bnb_8bit 58output_dir: miner_id_24 59pad_to_sequence_len: true 60resume_from_checkpoint: null 61s2_attention: null 62sample_packing: false 63save_steps: 200 64sequence_len: 512 65strict: false 66tf32: false 67tokenizer_type: AutoTokenizer 68train_on_inputs: false 69trust_remote_code: true 70val_set_size: 0.005 71wandb_entity: null 72wandb_mode: online 73wandb_name: 8a000dd1-5b3f-47db-9e70-f522ce6599ed 74wandb_project: Gradients-On-Demand 75wandb_run: your_name 76wandb_runid: 8a000dd1-5b3f-47db-9e70-f522ce6599ed 77warmup_steps: 30 78weight_decay: 0.0 79xformers_attention: null 80

39d93b8a-13bf-40e9-8ba8-8d338e0337b1

• Loss: 1.0696

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• 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: 30
• num_epochs: 3

Training results

Framework versions

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