WandB: https://wandb.ai/oaaic/orpo-llama-3/runs/gc2d3cxp
Benchmarks: TBD
axolotl version: 0.4.0
base_model: winglian/meta-llama3-chatml
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_4bit: true
rl: orpo
orpo_alpha: 0.1
chat_template: chatml
datasets:
- path: mlabonne/orpo-dpo-mix-40k
type: chat_template.argilla
chat_template: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./llama-3-orpo-qlora
sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false
adapter: qlora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
wandb_project: orpo-llama-3
wandb_entity: oaaic
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 8
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1.4e-5
max_grad_norm: 1.0
train_on_inputs: false
group_by_length: false
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 5
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
llama-3-orpo-qlora
This model was trained from scratch on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 1241
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0