LLama-8B-Instruct-v0.1-MI-2e-5

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: 1.2552
• Rewards/chosen: -0.5792
• Rewards/rejected: -0.7270
• Rewards/accuracies: 0.6626
• Rewards/margins: 0.1478
• Logps/rejected: -0.7270
• Logps/chosen: -0.5792
• Logits/rejected: -0.3939
• Logits/chosen: -0.3830

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: 2e-05
• train_batch_size: 2
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 16
• 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: cosine
• 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
1.2571	0.8550	400	1.2552	-0.5792	-0.7270	0.6626	0.1478	-0.7270	-0.5792	-0.3939	-0.3830

Framework versions

• Transformers 4.42.0
• Pytorch 2.1.2+cu121
• Datasets 2.14.6
• Tokenizers 0.19.1