Instructions to use tanmayakaranth/matcha-chartqa-lora-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tanmayakaranth/matcha-chartqa-lora-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/matcha-base") model = PeftModel.from_pretrained(base_model, "tanmayakaranth/matcha-chartqa-lora-adapter") - Transformers
How to use tanmayakaranth/matcha-chartqa-lora-adapter with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tanmayakaranth/matcha-chartqa-lora-adapter", dtype="auto") - Notebooks
- Google Colab
- Kaggle
matcha-chartqa-lora-adapter
This model is a fine-tuned version of google/matcha-base 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.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 50
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
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Model tree for tanmayakaranth/matcha-chartqa-lora-adapter
Base model
google/matcha-base