Instructions to use Shunchang/tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shunchang/tmp with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Shunchang/tmp", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolVLM-Base
tags:
- generated_from_trainer
model-index:
- name: tmp
results: []
tmp
This model is a fine-tuned version of HuggingFaceTB/SmolVLM-Base on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- 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: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1