See axolotl config

axolotl version: 0.4.1

yaml

1adapter: lora 2base_model: unsloth/Llama-3.2-1B 3bf16: true 4chat_template: llama3 5dataset_prepared_path: null 6datasets: 7- data_files: 8 - 1ba279f183337309_train_data.json 9 ds_type: json 10 format: custom 11 path: /workspace/input_data/1ba279f183337309_train_data.json 12 type: 13 field_input: input 14 field_instruction: instruction 15 field_output: output 16 format: '{instruction} {input}' 17 no_input_format: '{instruction}' 18 system_format: '{system}' 19 system_prompt: '' 20debug: null 21device_map: 22 ? '' 23 : 0,1,2,3,4,5,6,7 24early_stopping_patience: 2 25eval_max_new_tokens: 128 26eval_steps: 400 27eval_table_size: null 28flash_attention: true 29gradient_accumulation_steps: 4 30gradient_checkpointing: true 31group_by_length: false 32hub_model_id: Alphatao/c1eac2fa-e009-4d0d-a76a-fe9c5473b8e5 33hub_repo: null 34hub_strategy: null 35hub_token: null 36learning_rate: 0.0002 37load_best_model_at_end: true 38load_in_4bit: false 39load_in_8bit: false 40local_rank: null 41logging_steps: 1 42lora_alpha: 32 43lora_dropout: 0.05 44lora_fan_in_fan_out: null 45lora_model_dir: null 46lora_r: 16 47lora_target_linear: true 48lora_target_modules: 49- q_proj 50- k_proj 51- v_proj 52- o_proj 53- down_proj 54- up_proj 55lr_scheduler: cosine 56max_grad_norm: 1.0 57max_steps: 11709 58micro_batch_size: 2 59mlflow_experiment_name: /tmp/1ba279f183337309_train_data.json 60model_type: AutoModelForCausalLM 61num_epochs: 2 62optimizer: adamw_bnb_8bit 63output_dir: miner_id_24 64pad_to_sequence_len: true 65resume_from_checkpoint: null 66s2_attention: null 67sample_packing: false 68save_steps: 400 69sequence_len: 2048 70strict: false 71tf32: true 72tokenizer_type: AutoTokenizer 73train_on_inputs: false 74trust_remote_code: true 75val_set_size: 0.05 76wandb_entity: null 77wandb_mode: online 78wandb_name: 42ec8344-4e7f-44ae-abba-aec8960a0c35 79wandb_project: Gradients-On-Demand 80wandb_run: your_name 81wandb_runid: 42ec8344-4e7f-44ae-abba-aec8960a0c35 82warmup_steps: 10 83weight_decay: 0.0 84xformers_attention: null 85

c1eac2fa-e009-4d0d-a76a-fe9c5473b8e5

• Loss: 0.8032

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: 10
• training_steps: 11709

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