Tim Dolan
commited on
added tiny llama examples for lora and qlora (#1027)
Browse files* added tiny llama examples for lora and qlora
* corrected yml files and removed tiny-llama.yml from llama-2 example
examples/tiny-llama/README.md
ADDED
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# Overview
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This is a simple example of how to finetune TinyLlama1.1B using either lora or qlora:
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LoRa:
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```
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accelerate launch -m axolotl.cli.train examples/tiny-llama/lora.yml
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```
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qLoRa:
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```
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accelerate launch -m axolotl.cli.train examples/tiny-llama/qlora.yml
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```
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Both take about 10 minutes to complete on a 4090.
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examples/{llama-2/tiny-llama.yml → tiny-llama/lora.yml}
RENAMED
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base_model:
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model_type: LlamaForCausalLM
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tokenizer_type: LlamaTokenizer
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is_llama_derived_model: true
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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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warmup_steps: 10
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evals_per_epoch: 4
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eval_table_size:
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saves_per_epoch: 1
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debug:
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deepspeed:
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fsdp:
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fsdp_config:
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special_tokens:
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eos_token: "</s>"
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unk_token: "<unk>"
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
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model_type: LlamaForCausalLM
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tokenizer_type: LlamaTokenizer
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is_llama_derived_model: true
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sequence_len: 4096
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sample_packing: true
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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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warmup_steps: 10
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evals_per_epoch: 4
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saves_per_epoch: 1
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debug:
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deepspeed:
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fsdp:
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fsdp_config:
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special_tokens:
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examples/tiny-llama/qlora.yml
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
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model_type: LlamaForCausalLM
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tokenizer_type: LlamaTokenizer
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is_llama_derived_model: true
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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:
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val_set_size: 0.05
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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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sample_packing: true
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pad_to_sequence_len: true
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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_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: 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: false
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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:
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flash_attention: true
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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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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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