Instructions to use Asna-DifiNative/AIBuddy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Asna-DifiNative/AIBuddy with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Asna-DifiNative/AIBuddy") model = AutoModelForSeq2SeqLM.from_pretrained("Asna-DifiNative/AIBuddy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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# Fine-Tuned BART Model for Translation
This repository contains a fine-tuned version of the BART model for translation tasks. The model is trained to translate human instructions into CLI (Command Line Interface) commands.
## Model Details
- Model: BART
- Architecture: `facebook/bart-large`
- Fine-Tuned on: Custom translation dataset
- Max Input Length: 128 tokens
- Max Output Length: 50 tokens
- Beam Search: 4 beams
## Usage
You can use this model to generate CLI commands from human instructions.
You can either directly use the model for inference or integrate it into applications
using the provided Gradio interface.
### Inference
To perform inference using the model, you can load it.
### Gradio Interface
An interactive Gradio interface is provided for easy model interaction.
You can run the interface by executing the code in `gradio_app.py`.
The interface allows you to enter human instructions and get corresponding generated CLI commands.
## Model Files
- `config.json`: Model configuration file
- `pytorch_model.bin`: Model weights
- `tokenizer.json`: Tokenizer configuration file
- `vocab.txt`: Vocabulary file
## Acknowledgments
The initial BART model and tokenizer are from the Hugging Face Transformers library (`facebook/bart-large`).
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