step by step Quantization and Fine-Tuning of Large Language Model
Preparation
Data Collection and Preparation
Model Quantization
- Consider the specific requirements and constraints of the language model to determine the best quantization method.
- Choose between post-training quantization or quantization-aware training based on the available resources and the desired level of accuracy.
- Implement the selected quantization method on the language model.
- Follow the guidelines and instructions provided by the chosen quantization method's documentation or framework.
- Assess the quantized model's accuracy, inference speed, memory footprint, and other relevant metrics.
- Compare the performance of the quantized model with the original model to determine the impact of quantization on various aspects.
- Analyze the performance of the quantized model and identify any areas for improvement.
- Adjust the model's hyperparameters, training data, or optimization techniques to fine-tune the quantized model.
- Iteratively train and evaluate the model to achieve the desired accuracy and performance metrics.
Use in Manifestly