Instructions to use ayush-vatsal/fine-tuned_caption_falcon_7b_instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use ayush-vatsal/fine-tuned_caption_falcon_7b_instruct with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("ayush-vatsal/fine-tuned_caption_falcon_7b_instruct", set_active=True) - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
model-index:
- name: outputs
results: []
datasets:
- ayush-vatsal/description_to_caption
language:
- en
pipeline_tag: text2text-generation
library_name: adapter-transformers
outputs
This model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on a captions 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 30
Training results
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3