22810c97b7
* use warmup_ratio as a better default than warmup steps since it's data dependent * replace remainder of warmup_steps
75 lines
1.5 KiB
YAML
75 lines
1.5 KiB
YAML
base_model: LnL-AI/dbrx-base-converted-v2
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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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trust_remote_code: true
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datasets:
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- path: tatsu-lab/alpaca
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type: alpaca
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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/out
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sequence_len: 512
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sample_packing: false
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pad_to_sequence_len: false
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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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adapter: lora
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lora_model_dir:
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lora_r: 8
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lora_alpha: 16
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lora_dropout: 0.05
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# w1, w2, & v1 will hang the trainer
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lora_target_modules:
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- q_proj # attn
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- k_proj # attn
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- v_proj # attn
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- out_proj # attn
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- layer # router
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# - w1
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# - w2
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# - v1
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gradient_accumulation_steps: 1
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micro_batch_size: 1
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num_epochs: 1
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optimizer: paged_adamw_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: false # don't use with fsdp_activation_checkpointing
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gradient_checkpointing_kwargs:
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use_reentrant: false
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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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fsdp:
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- full_shard
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- auto_wrap
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fsdp_config:
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fsdp_limit_all_gathers: true
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fsdp_sync_module_states: true
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fsdp_offload_params: false
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fsdp_use_orig_params: false
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fsdp_cpu_ram_efficient_loading: true
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fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
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fsdp_transformer_layer_cls_to_wrap: DbrxBlock
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fsdp_state_dict_type: FULL_STATE_DICT
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fsdp_activation_checkpointing: true
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