WorldModel-Sciworld-Llama3.1-8B

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From Word to World: Can Large Language Models be Implicit Text-based World Models?

sciworld_train_with_env_40k

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the sciworld_train_with_env_40630 dataset.

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: 2
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 16
• 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: constant_with_warmup
• lr_scheduler_warmup_steps: 10
• num_epochs: 5.0

Training results

Framework versions

• Transformers 4.52.4
• Pytorch 2.9.0+cu128
• Datasets 3.6.0
• Tokenizers 0.21.1