A variety of large language models (LLMs) have demonstrated their capabilities in recent times. With the ever-advancing fields of artificial intelligence (ai), Natural Language Processing (NLP), and Natural Language Generation (NLG), these models have evolved and entered almost every industry. In the growing field of ai, it has become essential to have integration of text, images and sound to create complex models that can handle and analyze a variety of input sources.
In response to this, Fireworks.ai has posted ai/FireLLaVA-13b”>fireKEY, the first open source multimodal model under the Llama 2 community license that is commercially permissive. The team has shared that vision-language models (VLM) will be much more versatile with the FireLLaVA technique to understand both text cues and visual content.
Vision-language models (VLM) have been shown to be extremely useful in a variety of applications, including creating chatbots that can understand graphical data and creating marketing descriptions based on product photos. The well-known Visual Language Model (VLM), LLaVA, stands out for its remarkable performance in 11 tests. However, due to its non-commercial license, the open source version, LLaVA v1.5 13B, has restrictions on commercial use.
This restriction has been addressed by ai/blog/firellava-the-first-commercially-permissive-oss-llava-model”>FireLLaVA, which is available for free download, experimentation, and project integration under a commercially permissive license.. Working beyond the potential of LLaVA, FireLLaVA uses a generic architecture and training methodology to enable the language model to understand and respond to textual and visual input with equal efficiency.
FireLLaVA was developed with the idea of working with a wide range of real-world applications, such as answering photo-based questions and deciphering complex data sources, improving the accuracy and breadth of ai-based insights.
Training data is a major hurdle in developing models that can be used commercially. Despite being open source, the original LLaVA model had limitations because it was licensed under non-commercial terms and was trained using data provided by GPT-4. At FireLLaVA, the team has adopted a unique strategy of generating and training data using only open source software (OSS) models.
To balance model quality and efficiency, the team has used the language-only OSS CodeLlama 34B Instruct model to replicate the training data. Upon evaluation, the team shared that the resulting FireLLaVA model performed comparable to the original LLaVA model on several benchmarks. FireLLaVA performed better than the original model on four of the seven benchmarks, demonstrating the effectiveness of starting a language-only model for creating high-quality VLM model training data.
The team has shared that ai/FireLLaVA-13b”>fireKEY allows developers to easily incorporate vision-enabled features into their apps using its chat completion and completion APIs, as the API interface supports OpenAI Vision models. The team has shared some demonstration examples of using the model on the project website. In one example, the model was provided with an image of a train crossing a bridge with the prompt to describe the scene in the image, which the model explained perfectly and provided an accurate description of the image and scene.
The launch of FireLLaVA is a notable advance in multimodal artificial intelligence. FireLLaVA's performance on benchmarks indicates a bright future for building flexible and cost-effective vision-language models.
Tanya Malhotra is a final year student of University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with a burning interest in acquiring new skills, leading groups and managing work in an organized manner.
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