9f824ef76a
* simplify the example configs to be more minimal and less daunting * drop empty s2_attention from example yamls
84 lines
1.7 KiB
YAML
84 lines
1.7 KiB
YAML
#Note that we are switching from the regular chat template to chatml.
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#If you experience problems with the special tokens, training for more epochs can help.
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#After training, merge the model before inference otherwise you might
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#face problems with the special tokens.
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base_model: mistralai/Mistral-7B-Instruct-v0.2
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# optionally might have model_type or tokenizer_type
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model_type: MistralForCausalLM
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tokenizer_type: LlamaTokenizer
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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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load_in_8bit: false
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load_in_4bit: true
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strict: false
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chat_template: chatml
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rl: dpo
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datasets:
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- path: olivermolenschot/alpaca_messages_dpo_test
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type: chat_template.default
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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_property_mappings:
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role: role
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content: content
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./outputs/dpo-qlora
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sequence_len: 2048
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sample_packing: false
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pad_to_sequence_len: true
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adapter: qlora
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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: 0.2
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lora_target_linear: true
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lora_target_modules:
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- gate_proj
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- down_proj
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- up_proj
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- q_proj
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- v_proj
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- k_proj
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- o_proj
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lora_modules_to_save:
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- embed_tokens
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- lm_head
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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: 16
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num_epochs: 6
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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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bf16: auto
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tf32: false
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gradient_checkpointing: true
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: false
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warmup_steps: 10
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evals_per_epoch: 4
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saves_per_epoch: 1
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weight_decay: 0.0
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special_tokens:
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bos_token: "<|im_start|>"
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eos_token: "<|im_end|>"
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