library_name, base_model, tags, datasets, model-index

library_name	base_model	tags	datasets	model-index
transformers	W-61/llama-3-8b-base-sft-hh-harmless-4xh200	alignment-handbook new-dpo generated_from_trainer	Anthropic/hh-rlhf	name results llama-3-8b-base-new-dpo-ultrafeedback-4xh200

llama-3-8b-base-new-dpo-ultrafeedback-4xh200

This model is a fine-tuned version of W-61/llama-3-8b-base-sft-hh-harmless-4xh200 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

• Loss: 0.5214
• Fcm Dpo/beta: 0.0836
• Fcm Dpo/q T: 0.3380
• Fcm Dpo/delta: -0.0050
• Fcm Dpo/margin: 11.8756
• Margin Dpo/margin Mean: 11.8756
• Margin Dpo/margin Std: 18.3875
• Logps/chosen: -96.2474
• Logps/rejected: -113.1114
• Logps/ref Chosen: -75.8693
• Logps/ref Rejected: -80.8577
• Logits/chosen: 0.3597
• Logits/rejected: 0.3046

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: 5e-07
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• total_eval_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Fcm Dpo/beta	Fcm Dpo/q T	Fcm Dpo/delta	Fcm Dpo/margin	Margin Dpo/margin Mean	Margin Dpo/margin Std	Logps/chosen	Logps/rejected	Logps/ref Chosen	Logps/ref Rejected	Logits/chosen	Logits/rejected
1.0502	0.3023	200	0.5717	0.0936	0.3525	0.0185	10.3675	10.3675	18.4204	-87.9072	-103.2631	-75.8693	-80.8577	0.4369	0.3875
1.0126	0.6047	400	0.5364	0.1033	0.3417	0.0098	9.4748	9.4748	15.2862	-92.9376	-107.4008	-75.8693	-80.8577	0.3564	0.3008
1.0944	0.9070	600	0.5214	0.0836	0.3380	-0.0050	11.8756	11.8756	18.3875	-96.2474	-113.1114	-75.8693	-80.8577	0.3597	0.3046

Framework versions

• Transformers 4.51.0
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.21.4