vapo_lora_all_data_iter_2

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>

iter_2

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

• Loss: 0.8061
• Rewards/chosen: -0.1849
• Rewards/rejected: -0.4492
• Rewards/accuracies: 0.9873
• Rewards/margins: 0.2643
• Logps/rejected: -4.4920
• Logps/chosen: -1.8486
• Logits/rejected: 1.5509
• Logits/chosen: 1.8515

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-06
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 16
• total_train_batch_size: 128
• total_eval_batch_size: 8
• 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

Framework versions

• PEFT 0.11.1
• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1