Instructions to use khizerali/llava-plastic-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use khizerali/llava-plastic-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("llava-hf/llava-1.5-7b-hf") model = PeftModel.from_pretrained(base_model, "khizerali/llava-plastic-lora") - Notebooks
- Google Colab
- Kaggle
llava-plastic-lora
This model is a fine-tuned version of llava-hf/llava-1.5-7b-hf 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: 1
- eval_batch_size: 8
- 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
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.14.0
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Downloads last month
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Model tree for khizerali/llava-plastic-lora
Base model
llava-hf/llava-1.5-7b-hf