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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README.md
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license: apache-2.0
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license: apache-2.0
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---
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# Fine-Tuned BART Model for Translation
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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.
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## Model Details
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- Model: BART
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- Architecture: `facebook/bart-large`
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- Fine-Tuned on: Custom translation dataset (details in `train_model.py`)
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- Max Input Length: 128 tokens
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- Max Output Length: 50 tokens
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- Beam Search: 4 beams
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## Usage
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You can use this model to generate CLI commands from human instructions.
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You can either directly use the model for inference or integrate it into applications
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using the provided Gradio interface.
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### Inference
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To perform inference using the model, you can load it.
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### Gradio Interface
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An interactive Gradio interface is provided for easy model interaction.
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You can run the interface by executing the code in `gradio_app.py`.
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The interface allows you to enter human instructions and get corresponding generated CLI commands.
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## Model Files
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- `config.json`: Model configuration file
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- `pytorch_model.bin`: Model weights
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- `tokenizer.json`: Tokenizer configuration file
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- `vocab.txt`: Vocabulary file
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## Acknowledgments
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The initial BART model and tokenizer are from the Hugging Face Transformers library (`facebook/bart-large`).
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