FinTral is a family of multimodal large language models (LLMs) designed for financial analysis, built on the Mistral-7B model. It integrates textual, numerical, tabular, and image data, and is trained on a large financial dataset called FinSet, which includes nine tasks and 23 datasets. FinTral is enhanced with domain-specific pretraining, instruction fine-tuning, and RLAIF training, and demonstrates exceptional zero-shot performance, outperforming ChatGPT-3.5 in all tasks and surpassing GPT-4 in five out of nine tasks. FinTral-DPO-T&R, which incorporates tools and retrieval methods, shows strong performance in real-time financial analysis and decision-making. The model is evaluated on a comprehensive benchmark of eight different tasks, and its performance is compared to other models, including GPT-4. FinTral also addresses financial hallucinations through advanced training methods and AI feedback. The model is capable of handling complex financial tasks, including chart understanding, sentiment analysis, and credit scoring. FinTral is also equipped with vision capabilities, allowing it to process visual data. The model is evaluated on various financial datasets, including FinTerms-MCQ and Finance Bench, and shows strong performance in reducing hallucinations and improving accuracy. The model is also discussed in terms of its limitations, including domain-specific adaptability, real-time data handling, and maintenance. The model is developed with ethical considerations in mind, including energy efficiency, data privacy, and responsible use. The model is released with strict guidelines for its use, particularly in real-world applications. The model is a significant advancement in financial technology, offering a powerful tool for financial analysis and decision-making.FinTral is a family of multimodal large language models (LLMs) designed for financial analysis, built on the Mistral-7B model. It integrates textual, numerical, tabular, and image data, and is trained on a large financial dataset called FinSet, which includes nine tasks and 23 datasets. FinTral is enhanced with domain-specific pretraining, instruction fine-tuning, and RLAIF training, and demonstrates exceptional zero-shot performance, outperforming ChatGPT-3.5 in all tasks and surpassing GPT-4 in five out of nine tasks. FinTral-DPO-T&R, which incorporates tools and retrieval methods, shows strong performance in real-time financial analysis and decision-making. The model is evaluated on a comprehensive benchmark of eight different tasks, and its performance is compared to other models, including GPT-4. FinTral also addresses financial hallucinations through advanced training methods and AI feedback. The model is capable of handling complex financial tasks, including chart understanding, sentiment analysis, and credit scoring. FinTral is also equipped with vision capabilities, allowing it to process visual data. The model is evaluated on various financial datasets, including FinTerms-MCQ and Finance Bench, and shows strong performance in reducing hallucinations and improving accuracy. The model is also discussed in terms of its limitations, including domain-specific adaptability, real-time data handling, and maintenance. The model is developed with ethical considerations in mind, including energy efficiency, data privacy, and responsible use. The model is released with strict guidelines for its use, particularly in real-world applications. The model is a significant advancement in financial technology, offering a powerful tool for financial analysis and decision-making.