Fine-tunning models.

FinTech Optimization.

I used Fine-tuning models ilike as a powerful technique for adapt a pre-trained model (Transfer Learning) to a specific finantial task or dataset, involving two key stages: optimization and inference. At the optimization stage was apply adjusting the model parameters to better fit the resolve finantial formules or dataset, was taked the pre-trained model's weights are used as a starting point, and the model is then trained further on the new data at real time, including steps like data preparation, model selection, hyperparameter tuning, training, and evaluation. Take months to the model has been optimized because i'd needs more GPU, Now you can use it to make predictions on new, unseen data during the inference stage, which involves preprocessing the input data, deploying the fine-tuned model in a production, with the new environment, make predictions, and continuously monitoring and maintaining the model's performance its more easy, now can to ensure high-quality results with less time and effort than training a model from scratch. Ping me..

You can use Enhancing a FinTech Model with Retrieval Augmented Generation In building a FinTech model, I explored Retrieval Augmented Generation (RAG) to fetch external financial data and enable in-context learning without costly LLM fine-tuning. Leveraging the pre-trained FinBert model's knowledge, I integrated semantic search, news sentiment analysis, and score functions like BM25 and TF-IDF to improve hyperparameter tuning and get more precise accuracy on the model. To enhance the retrieval and search capabilities, I incorporated various vector index technologies, including ChromaDB, Elasticsearch, Pinecone, Vespa, and Annoy, as a non-parametric component complementing the parametric LLM. While the embedding search may be crucial, further investigation is needed, and I focused on improving data for the ETL (Extract, Transform, Load) process as well.