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README.md
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library_name: transformers
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base_model: h2oai/h2o-danube2-1.8b-base
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---
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library_name: transformers
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base_model: h2oai/h2o-danube2-1.8b-base
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---
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# danube2-1.8b-ORPO
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ChatML tokens are added and first fine-tuned with BAdam and then QLoRA+ on mlabonne/orpo-dpo-mix-40k, but as SFT and not DPO, and using LLama-Factory.
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## Template
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```jinja
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<|im_start>user
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{{instruction}}<|im_end|>
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<|im_start>assistant
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{{response}}<|im_end>
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```
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## BAdam
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```yaml
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### model
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model_name_or_path: danube2-base-chatml
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### method
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stage: sft
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do_train: true
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finetuning_type: full
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use_badam: true
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badam_switch_mode: ascending
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badam_switch_interval: 50
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badam_verbose: 1
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badam_start_block: 12
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badam_mask_mode: scatter
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seed: 314
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### dataset
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dataset: orpo_sft_mix_40k
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template: ninja_chatml
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cutoff_len: 8192
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overwrite_cache: false
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preprocessing_num_workers: 12
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### output
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output_dir: orpo-chatml-badam
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logging_steps: 5
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save_steps: 1
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save_strategy: epoch
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plot_loss: true
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overwrite_output_dir: false
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### train
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per_device_train_batch_size: 2
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gradient_accumulation_steps: 8
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learning_rate: 0.00001
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num_train_epochs: 2
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lr_scheduler_type: cosine
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warmup_ratio: 0.01
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pure_bf16: true
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flash_attn: fa2
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### eval
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val_size: 0.01
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per_device_eval_batch_size: 1
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eval_strategy: steps
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eval_steps: 1000
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```
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### QLoRA+
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```yaml
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### model
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model_name_or_path: orpo-chatml-badam
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### method
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stage: sft
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do_train: true
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finetuning_type: lora
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lora_target: all
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loraplus_lr_ratio: 16.0
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lora_rank: 8
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lora_alpha: 16
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use_unsloth: true
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quantization_bit: 4
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upcast_layernorm: true
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seed: 31415
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### dataset
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dataset: orpo_sft_mix_40k
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template: hermes_chatml
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cutoff_len: 8192
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overwrite_cache: false
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preprocessing_num_workers: 12
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### output
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output_dir: orpo-chatml-badam/loraplus
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logging_steps: 1
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save_steps: 1
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save_strategy: epoch
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plot_loss: true
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overwrite_output_dir: false
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### train
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per_device_train_batch_size: 4
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gradient_accumulation_steps: 4
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learning_rate: 0.0001
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num_train_epochs: 2.0
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lr_scheduler_type: cosine
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warmup_ratio: 0.01
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bf16: true
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flash_attn: fa2
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### eval
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val_size: 0.02
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per_device_eval_batch_size: 1
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eval_strategy: steps
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eval_steps: 1000
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```
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