* use warmup_ratio as a better default than warmup steps since it's data dependent * replace remainder of warmup_steps
46 lines
876 B
YAML
46 lines
876 B
YAML
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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# optionally might have model_type or tokenizer_type
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model_type: LlamaForCausalLM
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tokenizer_type: LlamaTokenizer
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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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max_steps: 200
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pretraining_dataset:
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- path: allenai/c4
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name: en
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type: pretrain
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dataset_prepared_path:
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val_set_size: 0.0
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output_dir: ./outputs/model-out
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sequence_len: 2048
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sample_packing: true
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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: 4
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micro_batch_size: 2
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num_epochs: 4
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: auto
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tf32: false
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gradient_checkpointing: true
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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warmup_ratio: 0.1
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evals_per_epoch:
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saves_per_epoch: 1
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weight_decay: 0.0
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special_tokens:
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