In this project, we fine-tune the Mistral model using the LoRA (Low-Rank Adaptation) technique. The goal is to enhance the model's ability to adapt to new and specific data by improving its learning. Rather than adjusting all model parameters, LoRA focuses on making small changes to a subset of parameters, resulting in faster training and reduced memory consumption.
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Data Preprocessing
- Carefully preprocess the text data to remove any noise or irrelevant information.
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Model Tuning
- Fine-tune the Mistral model using the prepared training data and the LoRA technique. This step involves training the model and adjusting it to optimally adapt to the new data.
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Model Evaluation
- Evaluate the performance of the fine-tuned model on a test dataset to assess its accuracy and efficiency in text classification.
To get started with this project, follow these steps:
- Prepare your text data for preprocessing.
- Apply the LoRA technique to fine-tune the Mistral model.
- Evaluate the fine-tuned model to measure its performance.
- Mistral Model
- LoRA Library
- Python 3.x
- Relevant Python packages (e.g., TensorFlow, PyTorch)
Feel free to open issues or submit pull requests if you have suggestions for improvements or fixes.
This project is licensed under the MIT License - see the LICENSE file for details.