metadata

library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
  - alignment-handbook
  - trl
  - cpo
  - generated_from_trainer
  - trl
  - cpo
  - generated_from_trainer
datasets:
  - princeton-nlp/llama3-ultrafeedback
model-index:
  - name: llama3.1-cpo-full-0913
    results: []

llama3.1-cpo-full-0913

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the princeton-nlp/llama3-ultrafeedback dataset. It achieves the following results on the evaluation set:

Loss: 1.5934
Rewards/chosen: -15.4936
Rewards/rejected: -16.2190
Rewards/accuracies: 0.6261
Rewards/margins: 0.7255
Logps/rejected: -162.1901
Logps/chosen: -154.9355
Logits/rejected: -0.4926
Logits/chosen: -0.5160
Nll Loss: 0.4228

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: 1e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen	Nll Loss
1.9304	0.2311	100	1.7873	-14.9945	-15.3576	0.5804	0.3632	-153.5762	-149.9445	-0.3649	-0.3854	0.4085
1.6908	0.4623	200	1.6702	-15.6437	-16.2439	0.5978	0.6002	-162.4385	-156.4369	-0.3777	-0.4014	0.4252
1.6317	0.6934	300	1.6162	-15.4682	-16.1519	0.6152	0.6837	-161.5185	-154.6818	-0.4753	-0.4948	0.4202
1.62	0.9246	400	1.5947	-15.5964	-16.3155	0.6261	0.7192	-163.1553	-155.9637	-0.4910	-0.5144	0.4262

Framework versions

Transformers 4.44.2
Pytorch 2.3.1
Datasets 2.21.0
Tokenizers 0.19.1