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_j-full-0912
    results: []

llama3.1-cpo_j-full-0912

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: 1.4395
Rewards/chosen: -16.1609
Rewards/rejected: -16.9344
Rewards/accuracies: 0.6326
Rewards/margins: 0.7735
Logps/rejected: -169.3439
Logps/chosen: -161.6093
Logits/rejected: -0.3578
Logits/chosen: -0.3883
Nll Loss: 0.2841

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
1.7848	0.2311	100	1.6452	-15.3752	-15.7662	0.5804	0.3910	-157.6625	-153.7521	-0.3516	-0.3794	0.2719
1.5276	0.4623	200	1.5229	-15.8100	-16.4430	0.6043	0.6331	-164.4303	-158.0997	-0.3983	-0.4237	0.2748
1.4811	0.6934	300	1.4640	-16.0706	-16.8001	0.6130	0.7296	-168.0013	-160.7057	-0.4069	-0.4339	0.2804
1.4642	0.9246	400	1.4429	-16.1577	-16.9120	0.6304	0.7544	-169.1204	-161.5765	-0.3509	-0.3812	0.2845

Framework versions

Transformers 4.44.2
Pytorch 2.3.1
Datasets 2.21.0
Tokenizers 0.19.1