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* init * logx * updates * increase flexibility * add option for turns * up * init * style * minor tweaks * add preferenes * typo
47 lines
1.5 KiB
YAML
47 lines
1.5 KiB
YAML
model_name_or_path: meta-llama/Llama-3.1-8B
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model_revision: main
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use_flash_attn: true
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tokenizer_name: meta-llama/Llama-3.1-8B
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use_slow_tokenizer: true
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dataset_mixer:
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# base math datasets:
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natolambert/tulu-v2-sft-mixture-flan: 50000
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natolambert/tulu-v2-sft-mixture-cot: 49747
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ai2-adapt-dev/personahub_math_v4_149975: 149975
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AI-MO/NuminaMath-TIR: 72441
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# v3.4 datasets keeping (for now):
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HuggingFaceH4/no_robots: 9500
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allenai/openassistant-guanaco-reformatted: 7708
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ai2-adapt-dev/tulu_hard_coded_examples: 14
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ai2-adapt-dev/SciRIFF-train-mix-science: 10000
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ai2-adapt-dev/Table-GPT-All-train: 3000
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# other datasets:
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ai2-adapt-dev/personahub_ifdata_v1_29980: 29980
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ai2-adapt-dev/coconot-sft-reformat: 11477
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ai2-adapt-dev/openmath-2-gsm8k: 50000
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# testing:
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m-a-p/CodeFeedback-Filtered-Instruction: 50000
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ai2-adapt-dev/WildChat-1M-Full-GPT4-Only: 100000
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# safety data:
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ai2-adapt-dev/synthetic-finalresp-wildguarmixtrain: 86759
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ai2-adapt-dev/processed-wildjailbreak: 261559
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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: 2 # effective batch size 128 with 1 node
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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/ |