iter_1
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the pref_data_1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.9272
- Rewards/chosen: -0.0568
- Rewards/rejected: -0.1245
- Rewards/accuracies: 0.9809
- Rewards/margins: 0.0677
- Logps/rejected: -1.2445
- Logps/chosen: -0.5677
- Logits/rejected: 0.0790
- Logits/chosen: 0.5251
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: 1e-05
- 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
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Model tree for DongfuJiang/vapo_lora_all_data_iter_1
Base model
meta-llama/Meta-Llama-3-8B-Instruct