See axolotl config

axolotl version: 0.4.1

yaml

1adapter: lora 2base_model: microsoft/Phi-3-mini-4k-instruct 3bf16: true 4chat_template: llama3 5dataset_prepared_path: null 6datasets: 7- data_files: 8 - 8b54a694ba612e8a_train_data.json 9 ds_type: json 10 format: custom 11 path: /workspace/input_data/8b54a694ba612e8a_train_data.json 12 type: 13 field_instruction: dataset 14 field_output: context 15 format: '{instruction}' 16 no_input_format: '{instruction}' 17 system_format: '{system}' 18 system_prompt: '' 19debug: null 20deepspeed: null 21early_stopping_patience: 2 22eval_max_new_tokens: 128 23eval_steps: 100 24eval_table_size: null 25flash_attention: false 26fp16: null 27fsdp: null 28fsdp_config: null 29gradient_accumulation_steps: 8 30gradient_checkpointing: true 31group_by_length: false 32hub_model_id: Alphatao/4290eedf-9e6b-4abb-909a-7fb37fab81c2 33hub_repo: null 34hub_strategy: checkpoint 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: null 49lr_scheduler: cosine 50max_grad_norm: 1.0 51max_steps: 2346 52micro_batch_size: 4 53mlflow_experiment_name: /tmp/8b54a694ba612e8a_train_data.json 54model_type: AutoModelForCausalLM 55num_epochs: 2 56optimizer: adamw_bnb_8bit 57output_dir: miner_id_24 58pad_to_sequence_len: true 59resume_from_checkpoint: null 60s2_attention: null 61sample_packing: false 62save_steps: 100 63sequence_len: 1024 64strict: false 65tf32: true 66tokenizer_type: AutoTokenizer 67train_on_inputs: false 68trust_remote_code: true 69val_set_size: 0.027907236346384617 70wandb_entity: null 71wandb_mode: online 72wandb_name: ce3ac79f-e735-4be7-a402-a6baba9386ed 73wandb_project: Gradients-On-Demand 74wandb_run: your_name 75wandb_runid: ce3ac79f-e735-4be7-a402-a6baba9386ed 76warmup_steps: 10 77weight_decay: 0.0 78xformers_attention: null 79

4290eedf-9e6b-4abb-909a-7fb37fab81c2

• Loss: 0.9365

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• 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_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: 2346

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