feat/llama-2 examples (#319)
* qlora llama-2 * qlora llama-2 * linting * readme * lora added * linting * change group_by_length * 13b fitting on 24gb * grouped lengths true * add pad token * change out dir --------- Co-authored-by: Mads Henrichsen <mads@Brbar-tilhrende-Mads.local>
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examples/llama-2/README.md
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examples/llama-2/README.md
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# Overview
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This is an example of a llama-2 configuration for 7b and 13b. The yaml file contains configuration for the 7b variant, but you can just aswell use the same settings for 13b.
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The 7b variant fits on any 24GB VRAM GPU and will take up about 17 GB of VRAM during training if using qlora and 20 GB if using lora. On a RTX 4090 it trains 3 epochs of the default dataset in about 15 minutes.
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The 13b variant will fit if you change these settings to these values:
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gradient_accumulation_steps: 2
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micro_batch_size: 1
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```shell
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accelerate launch scripts/finetune.py examples/llama-2/qlora.yml
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```
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or
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```shell
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accelerate launch scripts/finetune.py examples/llama-2/lora.yml
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```
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examples/llama-2/lora.yml
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examples/llama-2/lora.yml
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base_model: meta-llama/Llama-2-7b-hf
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base_model_config: meta-llama/Llama-2-7b-hf
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model_type: LlamaForCausalLM
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tokenizer_type: LlamaTokenizer
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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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datasets:
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- path: mhenrichsen/alpaca_2k_test
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type: alpaca
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.01
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output_dir: ./lora-out
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sequence_len: 4096
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max_packed_sequence_len: 4096
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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_watch:
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wandb_run_id:
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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: 3
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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: true
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bf16: true
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fp16: false
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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: true
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flash_attention:
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warmup_steps: 10
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eval_steps: 20
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save_steps:
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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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special_tokens:
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bos_token: "<s>"
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eos_token: "</s>"
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unk_token: "<unk>"
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pad_token: "<pad>"
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examples/llama-2/qlora.yml
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examples/llama-2/qlora.yml
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base_model: meta-llama/Llama-2-7b-hf
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base_model_config: meta-llama/Llama-2-7b-hf
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model_type: LlamaForCausalLM
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tokenizer_type: LlamaTokenizer
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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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datasets:
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- path: mhenrichsen/alpaca_2k_test
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type: alpaca
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.01
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output_dir: ./qlora-out
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adapter: qlora
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lora_model_dir:
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sequence_len: 4096
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max_packed_sequence_len: 4096
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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_modules:
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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_watch:
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wandb_run_id:
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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: 3
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optimizer: paged_adamw_32bit
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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: true
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bf16: true
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fp16: false
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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: true
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flash_attention:
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warmup_steps: 10
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eval_steps: 20
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save_steps:
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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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special_tokens:
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bos_token: "<s>"
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eos_token: "</s>"
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unk_token: "<unk>"
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pad_token: "<pad>"
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