71 lines
2.2 KiB
Markdown
71 lines
2.2 KiB
Markdown
---
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library_name: transformers
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license: other
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base_model: Qwen/Qwen2.5-7B
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: textworld_train_40k
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results: []
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---
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# *From Word to World*: Can Large Language Models be Implicit Text-based World Models?
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[](https://arxiv.org/abs/2512.18832)
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[](https://macaron.im/mindlab/research/how-world-models-unlock-scalable-agentic-rl)
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[](https://huggingface.co/papers/2512.18832)
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[](https://huggingface.co/collections/X1AOX1A/llm-as-world-models)
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[](https://huggingface.co/datasets/X1AOX1A/LLMasWorldModels)
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# textworld_train_40k
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the textworld_train_58805 dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 2
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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- total_eval_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: constant_with_warmup
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 5.0
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### Training results
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### Framework versions
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- Transformers 4.52.4
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- Pytorch 2.9.0+cu128
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- Datasets 3.6.0
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- Tokenizers 0.21.1
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