132eb740f0
* wip for dbrx finetuning * add fastcore for parallel loading of sharded weights * fix dtype for load, use PartialState instead of accelerator to init process group, remove redundant wandb callback * update to use v2 of the converted model * more fixes for dbrx loras * make sure to enable fsdp activation checkpointing * fix support for 8bit loras too for dbrx * apply z3 leaf moe fix for DBRX with deepspeed * don't raise value error since child module searches could fail and be ok * revert a previous change to fix fsdp * update mistral/mistral qlora+fsdp yamls * fix qlora+fsdp quant storage type * more edge cases for qlora-fsdp * fixes for fsdp+qlora w optimizer in 8bit * add bigstral z3 config and make sure to use full_state_dict for fsdp
64 lines
1.2 KiB
YAML
64 lines
1.2 KiB
YAML
base_model: mistral-community/Mixtral-8x22B-v0.1
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model_type: AutoModelForCausalLM
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tokenizer_type: LlamaTokenizer
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trust_remote_code: true
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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unfrozen_parameters:
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- ^lm_head.weight$
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- ^model.embed_tokens.weight$
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- model.layers.4[4-9]+.block_sparse_moe.gate
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- model.layers.4[4-9]+.block_sparse_moe.experts
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- model.layers.5[0-5]+.block_sparse_moe.gate
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- model.layers.5[0-5]+.block_sparse_moe.experts
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model_config:
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output_router_logits: 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: 0.05
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output_dir: ./out
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sequence_len: 2048
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sample_packing: true
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pad_to_sequence_len: true
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gradient_accumulation_steps: 1
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micro_batch_size: 1
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num_epochs: 3
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0001
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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save_total_limit: 1
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save_steps:
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debug:
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deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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eos_token: "<|im_end|>"
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tokens:
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- "<|im_start|>"
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