Fine-tuning large language models (LLMs) involves adapting a pre-trained model to specific tasks or domains by training it further on a smaller, task-specific dataset. This process leverages the general knowledge the model has already learned during its initial training on vast and diverse datasets, allowing it to specialize efficiently. Fine-tuning can improve performance in areas like sentiment analysis, text summarization, or domain-specific applications (e.g., legal or medical texts). Techniques such as supervised fine-tuning, reinforcement learning, and prompt engineering are commonly used to align the model with desired outcomes. Fine-tuning is a cost-effective way to harness the power of LLMs for targeted applications while minimizing computational overhead..
If you find this project useful, please give it a star to show your support and help others discover it!To get started with this project, clone the repository using the following command:
git clone https://github.com/TruongNV-hut/AIcandy_LLM_Finetuning_bloom_560m_iehimqko.gitBefore running the scripts, you need to install the required libraries. You can do this using pip:
pip install -r requirements.txtTo train the model and test, use the following command:
python AIcandy_LLM_Finetuing_bloom_560m_icsgpvrs.pyTo learn more about this project, see here.
To learn more about knowledge and real-world projects on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), visit the website aicandy.vn.
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