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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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# qwen2_5vl_7b_full_sft_251013_all_wo_hint-EMA
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This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the sft_data_vsi_wo_video_hint_sharegpt, the sft_data_gmai_reasoning_wo_image_hint_sharegpt, the sft_data_mmpr_wo_image_hint_sharegpt, the sft_data_mmk12_wo_image_hint_sharegpt, the sft_data_longvila_wo_video_hint_sharegpt and the sft_data_cosyn_chart_wo_image_hint_sharegpt datasets.
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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: 8
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- total_train_batch_size: 16
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- total_eval_batch_size: 64
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- optimizer: Use 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: 0.1
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- num_epochs: 1.0
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### Training results
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### Framework versions
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- Transformers 4.52.1
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- Pytorch 2.5.1
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- Datasets 3.6.0
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- Tokenizers 0.21.1
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## PyVision-RL: Forging Open Agentic Vision Models via RL
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This is PyVision-Video-7B-SFT, post trained from Qwen2.5-VL-7B.
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```bibtex
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@article{pyvision2025,
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title={PyVision-RL: Forging Open Agentic Vision Models},
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author={Your Name},
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journal={arXiv:2501.xxxxx},
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year={2025}
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}
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```
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