config-lora.yml

base_model: replit/replit-code-v1-3b
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

trust_remote_code: true
load_in_8bit: false
datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
adapter: lora
lora_model_dir:
sequence_len: 2048
max_packed_sequence_len:
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
  - Wqkv
  - mlp_up
  - mlp_down
wandb_project: lora-replit
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/lora-replit
batch_size: 8
micro_batch_size: 1
num_epochs: 4
optimizer:
torchdistx_path:
lr_scheduler:
learning_rate: 0.00001
bf16: auto
tf32: true
gradient_checkpointing:
resume_from_checkpoint:
logging_steps: 1
flash_attention:
gptq_groupsize:
gptq_model_v1:
warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0
#special_tokens: