LLM As World Models
Collection
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Updated
From Word to World: Can Large Language Models be Implicit Text-based World Models?
textworld_train_40k
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the textworld_train_58805 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
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Model tree for X1AOX1A/WorldModel-Textworld-Llama3.1-8B
Base model
meta-llama/Llama-3.1-8B