985819d89b
* Implementing a basic chat_template strategy for DPO datasets This mimics the sft chat_template strategy such that users can: * Specify the messages field * Specify the per message role and content fields * speicfy the chosen and rejected fields * Let the tokenizer construct the raw prompt * Ensure the chosen and rejected fields don't have any prefix tokens * Adding additional dpo chat template unittests * Rename test class
82 lines
1.4 KiB
YAML
82 lines
1.4 KiB
YAML
base_model: meta-llama/Meta-Llama-3-8B-Instruct
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: true
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load_in_4bit: false
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strict: false
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chat_template: llama3
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rl: dpo
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datasets:
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- path: fozziethebeat/alpaca_messages_2k_dpo_test
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type: chat_template.default
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chat_template: llama3
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field_messages: conversation
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field_chosen: chosen
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field_rejected: rejected
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message_field_role: role
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message_field_content: content
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roles:
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system:
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- system
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user:
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- user
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assistant:
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- assistant
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./outputs/lora-out
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sequence_len: 4096
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sample_packing: false
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pad_to_sequence_len: true
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adapter: lora
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lora_model_dir:
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_linear: true
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lora_fan_in_fan_out:
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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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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 4
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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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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s2_attention:
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warmup_steps: 10
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evals_per_epoch: 4
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eval_table_size:
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eval_max_new_tokens: 128
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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