ft-orpo-smollm-135M-instruct-on-hf-ultrafeedback

This model is a fine-tuned version of HuggingFaceTB/SmolLM-135M-Instruct on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

• Loss: 1.1646
• Rewards/chosen: -0.1296
• Rewards/rejected: -0.1298
• Rewards/accuracies: 0.4000
• Rewards/margins: 0.0002
• Logps/rejected: -1.2981
• Logps/chosen: -1.2964
• Logits/rejected: 31.6875
• Logits/chosen: 31.3425
• Nll Loss: 1.0873
• Log Odds Ratio: -0.7727
• Log Odds Chosen: -0.0238

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: 0.0003
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• 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	Log Odds Ratio	Log Odds Chosen
1.4274	0.27	100	1.2066	-0.1351	-0.1347	0.4100	-0.0004	-1.3467	-1.3508	28.6347	28.3442	1.1292	-0.7736	-0.0347
1.1351	0.53	200	1.1796	-0.1316	-0.1316	0.4100	0.0000	-1.3162	-1.3158	31.1292	30.7764	1.1024	-0.7723	-0.0251
1.135	0.8	300	1.1646	-0.1296	-0.1298	0.4000	0.0002	-1.2981	-1.2964	31.6875	31.3425	1.0873	-0.7727	-0.0238

Framework versions

• Transformers 4.39.3
• Pytorch 2.1.2
• Datasets 2.18.0
• Tokenizers 0.15.2