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model:
_component_: torchtune.models.llama3_1.qlora_llama3_1_8b
lora_attn_modules:
- q_proj
- v_proj
- k_proj
- output_proj
apply_lora_to_mlp: true
apply_lora_to_output: false
lora_rank: 8
lora_alpha: 16
tokenizer:
_component_: torchtune.models.llama3.llama3_tokenizer
path: /tmp/Meta-Llama-3.1-8B-Instruct/original/tokenizer.model
max_seq_len: null
checkpointer:
_component_: torchtune.training.FullModelHFCheckpointer
checkpoint_dir: /tmp/Meta-Llama-3.1-8B-Instruct/
checkpoint_files:
- model-00001-of-00004.safetensors
- model-00002-of-00004.safetensors
- model-00003-of-00004.safetensors
- model-00004-of-00004.safetensors
recipe_checkpoint: null
output_dir: /tmp/Meta-Llama-3.1-8B-Instruct/
model_type: LLAMA3
resume_from_checkpoint: false
save_adapter_weights_only: false
dataset:
_component_: torchtune.datasets.alpaca_dataset
source: b-r-ve/alpaca_fare_rules_shorter_length_1000_aed_2024_all_with_airline_30_08_24
train_on_input: true
split: train[:100%]
seed: null
shuffle: true
batch_size: 2
optimizer:
_component_: torch.optim.AdamW
weight_decay: 0.01
lr: 0.0003
lr_scheduler:
_component_: torchtune.modules.get_cosine_schedule_with_warmup
num_warmup_steps: 100
loss:
_component_: torchtune.modules.loss.CEWithChunkedOutputLoss
epochs: 1
max_steps_per_epoch: null
gradient_accumulation_steps: 16
compile: false
output_dir: /tmp/qlora_finetune_output/
metric_logger:
_component_: torchtune.training.metric_logging.WandBLogger
log_dir: ${output_dir}
project: torchtune_llama3_1_8B_qlora_single_device
log_every_n_steps: 1
log_peak_memory_stats: false
device: cuda
dtype: bf16
enable_activation_checkpointing: true
profiler:
_component_: torchtune.training.setup_torch_profiler
enabled: false
output_dir: ${output_dir}/profiling_outputs
cpu: true
cuda: true
profile_memory: false
with_stack: false
record_shapes: true
with_flops: false
wait_steps: 5
warmup_steps: 5
active_steps: 2
num_cycles: 1
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