Instructions to use harsh13333/gemma-clip-vqa_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harsh13333/gemma-clip-vqa_v1 with Transformers:
# Load model directly from transformers import GemmaCLIPVLM model = GemmaCLIPVLM.from_pretrained("harsh13333/gemma-clip-vqa_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from harsh13333/gemma-clip-vqa_v1: direct link, hf CLI and curl.
- Browser
- Download file 1.15 kB
-
https://huggingface.co/harsh13333/gemma-clip-vqa_v1/resolve/main/README.md
- Command line
-
hf download hf://harsh13333/gemma-clip-vqa_v1/README.md
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curl -L -o README.md https://huggingface.co/harsh13333/gemma-clip-vqa_v1/resolve/main/README.md
1.15 kB
metadata
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: gemma-clip-vqa_v1
results: []
gemma-clip-vqa_v1
This model is a fine-tuned version of on an unknown 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2