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tokenizer:
_component_: torchtune.models.mistral.mistral_tokenizer
path: /tmp/Mistral-7B-Instruct-v0.2/tokenizer.model
dataset:
_component_: torchtune.datasets.instruct_dataset
source: prithviraj-maurya/legalbench-entire
template: AlpacaInstructTemplate
column_map:
instruction: instruction
input: question
output: answer
max_seq_len: 256
train_on_input: true
split: train
seed: null
shuffle: true
model:
_component_: torchtune.models.mistral.qlora_mistral_7b
lora_attn_modules:
- q_proj
- k_proj
- v_proj
apply_lora_to_mlp: true
apply_lora_to_output: false
lora_rank: 64
lora_alpha: 16
checkpointer:
_component_: torchtune.utils.FullModelHFCheckpointer
checkpoint_dir: /tmp/Mistral-7B-Instruct-v0.2
checkpoint_files:
- pytorch_model-00001-of-00003.bin
- pytorch_model-00002-of-00003.bin
- pytorch_model-00003-of-00003.bin
recipe_checkpoint: null
output_dir: /tmp/Mistral-7B-Instruct-v0.2
model_type: MISTRAL
resume_from_checkpoint: false
optimizer:
_component_: torch.optim.AdamW
lr: 2.0e-05
lr_scheduler:
_component_: torchtune.modules.get_cosine_schedule_with_warmup
num_warmup_steps: 100
loss:
_component_: torch.nn.CrossEntropyLoss
batch_size: 4
epochs: 10
max_steps_per_epoch: 100000
gradient_accumulation_steps: 4
compile: false
device: cuda
enable_activation_checkpointing: true
dtype: fp32
output_dir: /logs/mistral_7b_qlora_single_device_finetune
metric_logger:
_component_: torchtune.utils.metric_logging.WandBLogger
project: torchtune
log_every_n_steps: 10
profiler:
_component_: torchtune.utils.profiler
enabled: false
output_dir: /tmp/alpaca-llama2-finetune/torchtune_perf_tracing.json
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