78 lines
1.4 KiB
YAML
78 lines
1.4 KiB
YAML
base_model: meta-llama/Llama-3.2-1B
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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load_in_8bit: false
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load_in_4bit: true
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strict: false
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rl: kto
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rl_beta: 0.5
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kto_desirable_weight: 0.2
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datasets:
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- path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
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type: llama3.ultra
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split: train
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.0
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output_dir: ./outputs/qlora-out
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remove_unused_columns: false
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adapter: qlora
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lora_model_dir:
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sequence_len: 2048
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sample_packing: false # not supported with kto
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eval_sample_packing: false
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pad_to_sequence_len: false
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lora_r: 32
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lora_alpha: 64
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lora_dropout: 0.05
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lora_target_linear: true
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lora_fan_in_fan_out:
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 1
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micro_batch_size: 2
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num_epochs: 1
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optimizer: adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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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: true
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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_steps: 20
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evals_per_epoch: 4
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eval_table_size:
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eval_max_new_tokens: 128
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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pad_token: "<|end_of_text|>"
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