base_model: EleutherAI/pythia-12b-deduped
base_model_ignore_patterns: pytorch*  # prefer safetensors
# optionally might have model_type or tokenizer_type
model_type: GPTNeoXForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
gptq: false
device_map: auto
datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
adapter:
lora_model_dir:
sequence_len: 2048
max_packed_sequence_len: 2048
lora_r: 64
lora_alpha: 32
lora_dropout: 0.0
lora_target_linear: true
lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/pythia-12b
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 5
learning_rate: 0.00003
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
bf16: false
fp16: false
float16: true
tf32: true
flash_optimum: true
resume_from_checkpoint:
gradient_checkpointing: true