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Geo-SFT – AI Model by miniHui | AlphaNeural AI | AlphaNeural AI

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miniHui/
Geo-SFT

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generated_from_trainer

Qwen/Qwen2.5-VL-7B-Instruct

text-generation-inference

endpoints_compatible

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sft

This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct on the cities_sft_16k 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-06
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 16
total_eval_batch_size: 64
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: 0.1
num_epochs: 2.0

Training results

Framework versions

Transformers 4.49.0
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.1