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axolotl/examples/llama-3/qlora-1b-kto.yaml

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base_model: meta-llama/Llama-3.2-1B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
load_in_8bit: false
load_in_4bit: true
rl: kto
rl_beta: 0.5
kto_desirable_weight: 0.2
datasets:
- path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
type: llama3.ultra
split: train
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/qlora-out
remove_unused_columns: false
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: false # not supported with kto
eval_sample_packing: false
pad_to_sequence_len: false
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
flash_attention: true
warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: "<|end_of_text|>"