base_model: Delta-Vector/Holland-4B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: NewEden/CivitAI-SD-Prompts # type: # system_prompt: "" # system_format: "<|im_start|>system\n{system}<|im_end|>\n" # field_system: instruction # field_instruction: input # field_input: "" # field_output: output # no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n" # system_prompt: "" # field_instruction: instruction # field_input: input # field_output: output # format: |- # <|im_start|>system # {instruction}<|im_end|> # <|im_start|>user # {input}<|im_end|> # <|im_start|>assistant # {output} type: alpaca conversation: mpt-30b-instruct # field_system: instruction # field_instruction: input # field_input: input # field_output: output chat_template: alpaca dataset_prepared_path: val_set_size: 0.02 output_dir: ./outputs/out2 sequence_len: 8192 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true adapter: lora_model_dir: lora_r: lora_alpha: lora_dropout: lora_target_linear: true lora_fan_in_fan_out: wandb_project: SDprompterV2 wandb_entity: wandb_watch: wandb_name: SDprompterV2 wandb_log_model: gradient_accumulation_steps: 32 micro_batch_size: 1 num_epochs: 2 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 0.00002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_ratio: 0.05 evals_per_epoch: 4 saves_per_epoch: 1 debug: weight_decay: 0.05 deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json special_tokens: pad_token: <|finetune_right_pad_id|>