See axolotl config

axolotl version: 0.4.1

yaml

1adapter: lora 2base_model: llamafactory/tiny-random-Llama-3 3bf16: true 4chat_template: llama3 5dataset_prepared_path: null 6datasets: 7- data_files: 8 - 2bfc56eb792607b0_train_data.json 9 ds_type: json 10 format: custom 11 path: /workspace/input_data/2bfc56eb792607b0_train_data.json 12 type: 13 field_input: hypothesis 14 field_instruction: premise 15 field_output: augmented_hypothesis 16 format: '{instruction} {input}' 17 no_input_format: '{instruction}' 18 system_format: '{system}' 19 system_prompt: '' 20debug: null 21deepspeed: null 22early_stopping_patience: 2 23early_stopping_threshold: 0.0001 24eval_max_new_tokens: 128 25eval_steps: 100 26eval_table_size: null 27flash_attention: true 28fp16: null 29fsdp: null 30fsdp_config: null 31gradient_accumulation_steps: 8 32gradient_checkpointing: true 33group_by_length: false 34hub_model_card: false 35hub_model_id: romainnn/c79eecb1-8fe2-4070-bd99-ba5979c50fb2 36hub_repo: null 37hub_strategy: checkpoint 38hub_token: null 39learning_rate: 0.0002 40load_best_model_at_end: true 41load_in_4bit: false 42load_in_8bit: false 43local_rank: null 44logging_steps: 1 45lora_alpha: 128 46lora_dropout: 0.3 47lora_fan_in_fan_out: null 48lora_model_dir: null 49lora_r: 64 50lora_target_linear: true 51lora_target_modules: 52- q_proj 53- k_proj 54- v_proj 55lr_scheduler: cosine 56max_grad_norm: 1.0 57max_steps: 5376 58micro_batch_size: 4 59mlflow_experiment_name: /tmp/2bfc56eb792607b0_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: 100 69sequence_len: 2048 70special_tokens: 71 pad_token: <|eot_id|> 72strict: false 73tf32: true 74tokenizer_type: AutoTokenizer 75train_on_inputs: false 76trust_remote_code: true 77val_set_size: 0.024827325948031442 78wandb_entity: null 79wandb_mode: online 80wandb_name: 09b4095a-cec4-4a0f-b7ab-08acea26803b 81wandb_project: Gradients-On-Demand 82wandb_run: your_name 83wandb_runid: 09b4095a-cec4-4a0f-b7ab-08acea26803b 84warmup_steps: 10 85weight_decay: 0.0 86xformers_attention: null 87

c79eecb1-8fe2-4070-bd99-ba5979c50fb2

• Loss: 11.6001

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: 5376

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