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33 lines
1.1 KiB
YAML
33 lines
1.1 KiB
YAML
model_name_or_path: meta-llama/Meta-Llama-3-8B
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model_revision: main
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use_flash_attn: true
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tokenizer_name: meta-llama/Meta-Llama-3-8B
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use_slow_tokenizer: true
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dataset_mixer:
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# Tulu V2 datasets
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ai2-adapt-dev/llama-3-tulu-v2-sft-mixture-with-subset-llama-405b-completions-code_alpaca-open_orca-gpt4_alpaca: 326154
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# Tulu V3 datasets (WIP)
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HuggingFaceH4/no_robots: 9500 # all
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ai2-adapt-dev/metamath-qa-reformat: 100000
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ai2-adapt-dev/codefeedback-single-turn-reformat: 156526 # all
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nvidia/Daring-Anteater: 99532 # all
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# tbd:
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# AI-MO/NuminaMath-TIR: 72441 # all
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# ai2-adapt-dev/slim-orca-300k: 100000
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max_seq_length: 4096 # Note, reduced from 8192 to fit on one GPU with DeepSpeed Stage3
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preprocessing_num_workers: 128
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per_device_train_batch_size: 1 # note, this is set up for 8 GPUs
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gradient_accumulation_steps: 4 # effective batch size 128 with 4 nodes
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learning_rate: 5.0e-06 # best LR so far
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lr_scheduler_type: linear
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warmup_ratio: 0.03
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weight_decay: 0.0
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num_train_epochs: 2
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output_dir: /output/
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with_tracking: true
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report_to:
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- wandb
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logging_steps: 1
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checkpointing_steps: epoch
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dataset_mix_dir: /output/
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