llama-3-8b-base-slic-hf-ultrafeedback-4xh200

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llama-3-8b-base-slic-hf-ultrafeedback-8xh200

This model is a fine-tuned version of W-61/llama-3-8b-base-sft-ultrachat-8xh200 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

• Loss: 341.9101
• Rewards/chosen: -260.8732
• Rewards/rejected: -246.9854
• Rewards/accuracies: 0.4919
• Rewards/margins: -13.8878
• Logps/chosen: -260.8732
• Logps/rejected: -246.9854
• Slic/rank Loss: 81.0369
• Slic/ce Loss: 260.8732
• Logits/chosen: -0.5998
• Logits/rejected: -0.6056

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: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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

Framework versions

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