lora-mps.yml

base_model: TinyLlama/TinyLlama_v1.1
# optionally might have model_type or tokenizer_type
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

load_in_8bit: true
load_in_4bit: false

datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0
output_dir: ./outputs/lora-out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0002

bf16: auto
fp16: false
tf32: true

gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: false

warmup_steps: 10
evals_per_epoch: 0
saves_per_epoch: 1
weight_decay: 0.0
special_tokens: