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added tiny llama examples for lora and qlora (axolotl-ai-cloud#1027)
* added tiny llama examples for lora and qlora * corrected yml files and removed tiny-llama.yml from llama-2 example
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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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``` | ||
accelerate launch -m axolotl.cli.train examples/tiny-llama/lora.yml | ||
``` | ||
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qLoRa: | ||
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``` | ||
accelerate launch -m axolotl.cli.train examples/tiny-llama/qlora.yml | ||
``` | ||
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Both take about 10 minutes to complete on a 4090. |
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T | ||
model_type: LlamaForCausalLM | ||
tokenizer_type: LlamaTokenizer | ||
is_llama_derived_model: true | ||
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load_in_8bit: false | ||
load_in_4bit: true | ||
strict: false | ||
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datasets: | ||
- path: mhenrichsen/alpaca_2k_test | ||
type: alpaca | ||
dataset_prepared_path: | ||
val_set_size: 0.05 | ||
output_dir: ./qlora-out | ||
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adapter: qlora | ||
lora_model_dir: | ||
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sequence_len: 4096 | ||
sample_packing: true | ||
pad_to_sequence_len: true | ||
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lora_r: 32 | ||
lora_alpha: 16 | ||
lora_dropout: 0.05 | ||
lora_target_modules: | ||
lora_target_linear: true | ||
lora_fan_in_fan_out: | ||
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wandb_project: | ||
wandb_entity: | ||
wandb_watch: | ||
wandb_name: | ||
wandb_log_model: | ||
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gradient_accumulation_steps: 4 | ||
micro_batch_size: 2 | ||
num_epochs: 4 | ||
optimizer: paged_adamw_32bit | ||
lr_scheduler: cosine | ||
learning_rate: 0.0002 | ||
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train_on_inputs: false | ||
group_by_length: false | ||
bf16: true | ||
fp16: false | ||
tf32: false | ||
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gradient_checkpointing: true | ||
early_stopping_patience: | ||
resume_from_checkpoint: | ||
local_rank: | ||
logging_steps: 1 | ||
xformers_attention: | ||
flash_attention: true | ||
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warmup_steps: 10 | ||
evals_per_epoch: 4 | ||
saves_per_epoch: 1 | ||
debug: | ||
deepspeed: | ||
weight_decay: 0.0 | ||
fsdp: | ||
fsdp_config: | ||
special_tokens: | ||
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