model: _component_: torchtune.models.llama3_1.qlora_llama3_1_8b lora_attn_modules: - q_proj - v_proj - k_proj - output_proj apply_lora_to_mlp: true apply_lora_to_output: false lora_rank: 8 lora_alpha: 16 tokenizer: _component_: torchtune.models.llama3.llama3_tokenizer path: /tmp/Meta-Llama-3.1-8B-Instruct/original/tokenizer.model max_seq_len: null checkpointer: _component_: torchtune.training.FullModelHFCheckpointer checkpoint_dir: /tmp/Meta-Llama-3.1-8B-Instruct/ checkpoint_files: - model-00001-of-00004.safetensors - model-00002-of-00004.safetensors - model-00003-of-00004.safetensors - model-00004-of-00004.safetensors recipe_checkpoint: null output_dir: /tmp/Meta-Llama-3.1-8B-Instruct/ model_type: LLAMA3 resume_from_checkpoint: false save_adapter_weights_only: false dataset: _component_: torchtune.datasets.alpaca_dataset source: b-r-ve/alpaca_fare_rules_shorter_length_1000_aed_2024_all_with_airline_04_09_24 train_on_input: true split: train[:100%] seed: null shuffle: true batch_size: 2 optimizer: _component_: torch.optim.AdamW weight_decay: 0.01 lr: 0.0003 lr_scheduler: _component_: torchtune.modules.get_cosine_schedule_with_warmup num_warmup_steps: 100 loss: _component_: torchtune.modules.loss.CEWithChunkedOutputLoss epochs: 1 max_steps_per_epoch: null gradient_accumulation_steps: 16 compile: false output_dir: /tmp/qlora_finetune_output/ metric_logger: _component_: torchtune.training.metric_logging.WandBLogger log_dir: ${output_dir} project: torchtune_llama3_1_8B_qlora_single_device log_every_n_steps: 1 log_peak_memory_stats: false device: cuda dtype: bf16 enable_activation_checkpointing: true profiler: _component_: torchtune.training.setup_torch_profiler enabled: false output_dir: ${output_dir}/profiling_outputs cpu: true cuda: true profile_memory: false with_stack: false record_shapes: true with_flops: false wait_steps: 5 warmup_steps: 5 active_steps: 2 num_cycles: 1