base_model: meta-llama/Meta-Llama-3-8B-Instruct model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false save_safetensors: true rl: dpo chat_template: chatml datasets: - path: Intel/orca_dpo_pairs split: train type: chatml.intel dataset_prepared_path: val_set_size: 0.1 output_dir: ./models/Meta-Llama-3-8B-instruct-DPO-v0.3 adapter: qlora lora_model_dir: sequence_len: 8192 sample_packing: false pad_to_sequence_len: false lora_r: 64 lora_alpha: 32 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_modules_to_save: - embed_tokens - lm_head wandb_project: llama-3-instruct-dpo wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 2 micro_batch_size: 1 num_epochs: 3 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 5e-6 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 100 evals_per_epoch: 1 eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 6 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: "<|end_of_text|>"