Instructions to use Koushik-Katkam/llava-1.5-7b-rsvqa-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Koushik-Katkam/llava-1.5-7b-rsvqa-adapter with PEFT:
Task type is invalid.
- Transformers
How to use Koushik-Katkam/llava-1.5-7b-rsvqa-adapter with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Koushik-Katkam/llava-1.5-7b-rsvqa-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
llava-1.5-7b-rsvqa-adapter
This model is a fine-tuned version of llava-hf/llava-1.5-7b-hf 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.20.0
- Transformers 5.16.1
- Pytorch 2.10.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.2
- Downloads last month
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Base model
llava-hf/llava-1.5-7b-hf