1b53c49e1a
* diffusion training plugin * cleanup * nits * fixes + improvements * add back in reinit_weights (clobbered?); masking / pretrain fixes * nits * cleanup; tests draft * sample generation, tests fixes * fixes * nits * add inference support; add auto-mask token support * nits * nits * progress * simplify logging * lint * prefix args with diffusion_ * coderabbito * tests fix * nit * nits * cleanup + nits * nits * fix SFT sample gen * fixes * fix * comments * comments * lint * reward model lora fix * cleanup; fix pretraining_dataset case * gradio inference * update cfgs * update cfgs * train, generation parity, cleanup * fix * simplify * test * test fix
60 lines
1.1 KiB
YAML
60 lines
1.1 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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datasets:
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- path: teknium/GPT4-LLM-Cleaned
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type: alpaca
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val_set_size: 0.05
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plugins:
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- axolotl.integrations.diffusion.DiffusionPlugin
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diffusion:
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noise_schedule: cosine
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min_mask_ratio: 0.1
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max_mask_ratio: 0.9
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num_diffusion_steps: 128
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eps: 1e-3
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importance_weighting: true
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mask_token_id: 128002
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generate_samples: true
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generation_interval: 250
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output_dir: ./outputs/model-out
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sequence_len: 512
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sample_packing: true
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eval_sample_packing: true
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gradient_accumulation_steps: 4
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micro_batch_size: 4
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num_epochs: 1
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warmup_steps: 0.1
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optimizer: adamw_8bit
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lr_scheduler: cosine
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learning_rate: 1e-5
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bf16: auto
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tf32: true
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gradient_checkpointing: true
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resume_from_checkpoint:
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sdp_attention: true
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logging_steps: 1
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save_strategy: best
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eval_strategy: epoch
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special_tokens:
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pad_token: "<|end_of_text|>"
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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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# save_first_step: true # uncomment this to validate checkpoint saving works with your config
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