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--- |
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base_model: henryhe0123/PC-Agent-E |
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datasets: |
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- henryhe0123/PC-Agent-E |
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library_name: transformers |
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license: mit |
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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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pipeline_tag: image-text-to-text |
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--- |
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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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# PC-Agent-E |
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This model is a fine-tuned version of [Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) on the PC-Agent-E dataset. |
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It was presented in [Efficient Agent Training for Computer Use](https://huggingface.co/papers/2505.13909). |
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Github repository: https://github.com/GAIR-NLP/PC-Agent-E |
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## Training procedure |
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Github repository: https://github.com/GAIR-NLP/PC-Agent-E |
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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: 2e-06 |
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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: 32 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 256 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 2 |
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### Framework versions |
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- Transformers 4.49.0.dev0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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