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64 lines
1.3 KiB
YAML
64 lines
1.3 KiB
YAML
base_model: microsoft/Phi-3-mini-4k-instruct
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# optionally might have model_type or tokenizer_type
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trust_remote_code: true
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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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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chat_template: phi_3
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datasets:
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- path: garage-bAInd/Open-Platypus
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type: alpaca:phi
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dataset_prepared_path:
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val_set_size: 0.01
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output_dir: ./out
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sequence_len: 4096
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sample_packing: true
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adapter: lora
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lora_model_dir:
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lora_r: 64
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lora_alpha: 32
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lora_dropout: 0.05
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lora_target_linear: true
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gradient_accumulation_steps: 1
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micro_batch_size: 2
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num_epochs: 1
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optimizer: adamw_torch_fused
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adam_beta2: 0.95
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adam_epsilon: 0.00001
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max_grad_norm: 1.0
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lr_scheduler: cosine
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learning_rate: 5.0e-6
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bf16: auto
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: True
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early_stopping_patience: 3
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logging_steps: 1
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attn_implementation: flash_attention_2
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eval_steps: 1000
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save_steps: 5000
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eval_batch_size: 2
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eval_sample_packing: false
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eval_table_size: 2
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eval_max_new_tokens: 32
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eval_causal_lm_metrics: ["perplexity"]
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do_causal_lm_eval: true
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warmup_ratio: 0.2
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debug: true
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weight_decay: 0.1
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resize_token_embeddings_to_32x: true
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# save_first_step: true # uncomment this to validate checkpoint saving works with your config
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