# 1b: tiiuae/falcon-rw-1b # 40b: tiiuae/falcon-40b base_model: tiiuae/falcon-7b # required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main trust_remote_code: true model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer is_falcon_derived_model: true load_in_8bit: false # enable 4bit for QLoRA load_in_4bit: true gptq: false strict: false push_dataset_to_hub: datasets: - path: QingyiSi/Alpaca-CoT data_files: - Chain-of-Thought/formatted_cot_data/gsm8k_train.json type: "alpaca:chat" dataset_prepared_path: val_set_size: 0.05 # enable QLoRA adapter: qlora lora_model_dir: sequence_len: 2048 max_packed_sequence_len: # hyperparameters from QLoRA paper Appendix B.2 # "We find hyperparameters to be largely robust across datasets" lora_r: 64 lora_alpha: 16 # 0.1 for models up to 13B # 0.05 for 33B and 65B models lora_dropout: 0.05 # add LoRA modules on all linear layers of the base model lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: output_dir: ./qlora-out # QLoRA paper Table 9 # - 16 for 7b & 13b # - 32 for 33b, 64 for 64b # Max size tested on A6000 # - 7b: 40 # - 40b: 4 # decrease if OOM, increase for max VRAM utilization micro_batch_size: 1 gradient_accumulation_steps: 2 num_epochs: 4 # Optimizer for QLoRA optimizer: paged_adamw_32bit torchdistx_path: lr_scheduler: cosine # QLoRA paper Table 9 # - 2e-4 for 7b & 13b # - 1e-4 for 33b & 64b learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true # stop training after this many evaluation losses have increased in a row # https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback early_stopping_patience: 3 resume_from_checkpoint: auto_resume_from_checkpoints: true local_rank: logging_steps: 1 xformers_attention: true flash_attention: gptq_groupsize: gptq_model_v1: warmup_steps: 10 evals_per_epoch: 4 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.000001 fsdp: fsdp_config: special_tokens: pad_token: "<|endoftext|>" bos_token: ">>ABSTRACT<<" eos_token: "<|endoftext|>"