---
base_model: henryhe0123/PC-Agent-E
datasets:
- henryhe0123/PC-Agent-E
library_name: transformers
license: mit
tags:
- llama-factory
- full
- generated_from_trainer
pipeline_tag: image-text-to-text
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# PC-Agent-E

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.

It was presented in [Efficient Agent Training for Computer Use](https://huggingface.co/papers/2505.13909).

Github repository: https://github.com/GAIR-NLP/PC-Agent-E

## Training procedure

Github repository: https://github.com/GAIR-NLP/PC-Agent-E

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 256
- 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: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2

### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0