Update sdprompter2.yaml
Browse files- sdprompter2.yaml +101 -99
sdprompter2.yaml
CHANGED
@@ -1,99 +1,101 @@
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base_model: Delta-Vector/Holland-4B
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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trust_remote_code: true
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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datasets:
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- path: NewEden/CivitAI-SD-Prompts
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# type:
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# system_prompt: ""
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# system_format: "<|im_start|>system\n{system}<|im_end|>\n"
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# field_system: instruction
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# field_instruction: input
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# field_input: ""
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# field_output: output
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# no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
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# system_prompt: ""
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# field_instruction: instruction
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# field_input: input
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# field_output: output
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# format: |-
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# <|im_start|>system
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# {instruction}<|im_end|>
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# <|im_start|>user
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# {input}<|im_end|>
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# <|im_start|>assistant
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# {output}
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type: alpaca
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conversation: mpt-30b-instruct
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# field_system: instruction
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# field_instruction: input
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# field_input: input
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# field_output: output
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chat_template: alpaca
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dataset_prepared_path:
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val_set_size: 0.02
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output_dir: ./outputs/out2
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sequence_len: 8192
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sample_packing: true
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eval_sample_packing: true
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pad_to_sequence_len: true
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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liger_rope: true
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liger_rms_norm: true
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liger_swiglu: true
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liger_fused_linear_cross_entropy: true
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adapter:
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lora_model_dir:
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lora_r:
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lora_alpha:
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lora_dropout:
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lora_target_linear: true
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lora_fan_in_fan_out:
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wandb_project: SDprompterV2
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wandb_entity:
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wandb_watch:
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wandb_name: SDprompterV2
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wandb_log_model:
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gradient_accumulation_steps: 32
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micro_batch_size: 1
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num_epochs: 2
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optimizer: adamw_torch
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lr_scheduler: cosine
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learning_rate: 0.00002
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_ratio: 0.05
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evals_per_epoch: 4
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saves_per_epoch: 1
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debug:
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weight_decay: 0.
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-
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base_model: Delta-Vector/Holland-4B
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+
model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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+
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trust_remote_code: true
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+
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load_in_8bit: false
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load_in_4bit: false
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+
strict: false
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10 |
+
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datasets:
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- path: NewEden/CivitAI-SD-Prompts
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+
# type:
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# system_prompt: ""
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# system_format: "<|im_start|>system\n{system}<|im_end|>\n"
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# field_system: instruction
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# field_instruction: input
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# field_input: ""
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# field_output: output
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# no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
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+
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# system_prompt: ""
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# field_instruction: instruction
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# field_input: input
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# field_output: output
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# format: |-
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# <|im_start|>system
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# {instruction}<|im_end|>
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# <|im_start|>user
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# {input}<|im_end|>
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# <|im_start|>assistant
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# {output}
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type: alpaca
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conversation: mpt-30b-instruct
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# field_system: instruction
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# field_instruction: input
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# field_input: input
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# field_output: output
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chat_template: alpaca
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+
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dataset_prepared_path:
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val_set_size: 0.02
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output_dir: ./outputs/out2
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sequence_len: 8192
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sample_packing: true
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eval_sample_packing: true
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pad_to_sequence_len: true
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+
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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+
liger_rope: true
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+
liger_rms_norm: true
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+
liger_swiglu: true
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liger_fused_linear_cross_entropy: true
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adapter:
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lora_model_dir:
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lora_r:
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lora_alpha:
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lora_dropout:
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lora_target_linear: true
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lora_fan_in_fan_out:
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wandb_project: SDprompterV2
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wandb_entity:
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wandb_watch:
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wandb_name: SDprompterV2
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wandb_log_model:
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+
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gradient_accumulation_steps: 32
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micro_batch_size: 1
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num_epochs: 2
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optimizer: adamw_torch
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lr_scheduler: cosine
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learning_rate: 0.00002
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+
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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+
fp16:
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tf32: true
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+
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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+
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warmup_ratio: 0.05
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evals_per_epoch: 4
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saves_per_epoch: 1
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debug:
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weight_decay: 0.05
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deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
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special_tokens:
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pad_token: <|finetune_right_pad_id|>
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