This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the princeton-nlp/llama3-ultrafeedback dataset. It achieves the following results on the evaluation set:

Loss: 0.7528
Original Losses: 2.0491
Weight: 0.3713
Abs Diff: 3.1759
Rewards/chosen: -45.3959
Rewards/rejected: -50.3664
Rewards/accuracies: 0.6976
Rewards/margins: 4.9705
Logps/rejected: -20.1465
Logps/chosen: -18.1584
Logits/rejected: 1.8309
Logits/chosen: 1.7177
All Logps 1: -7614.6904
All Logps 1 Values: -7614.6909
All Logps 2: 414.8609
All Logps 2 Values: 414.8609

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: 2
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Original Losses	Weight	Abs Diff	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen	All Logps 1	All Logps 1 Values	All Logps 2	All Logps 2 Values
0.7506	0.8549	400	0.7528	2.0491	0.3713	3.1759	-45.3959	-50.3664	0.6976	4.9705	-20.1465	-18.1584	1.8309	1.7177	-7614.6904	-7614.6909	414.8609	414.8609

Framework versions

Transformers 4.41.2
Pytorch 2.2.2+cu121
Datasets 2.20.0
Tokenizers 0.19.1

RAY2L/Llama-3-Instruct-8B-SimPOW-1

llama-3-8b-instruct-simpo

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RAY2L/
Llama-3-Instruct-8B-SimPOW-1