Llava 13B - Details

Llava 13B is a large language model developed by Liuhaotian/Llava-V1.6 with 13B parameters. It operates under the Apache License 2.0 (Apache-2.0), Llama 2 Community License Agreement (LLAMA-2-CLA), Llama 2 Community License Agreement (LLAMA-2-CLA). The model focuses on integrating a vision encoder with Vicuna to enable versatile visual and language understanding capabilities.

LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture. The LLaVA-v1.5-13B version was trained in September 2023 using a diverse dataset including 558K filtered image-text pairs from LAION/CC/SBU (captioned by BLIP), 158K GPT-generated multimodal instruction-following data, 450K academic-task-oriented VQA data, and 40K ShareGPT data. Designed for research on large multimodal models and chatbots, it combines visual and language understanding capabilities to support tasks requiring interaction with both text and images.

LLaVA 13B has 13B parameters, placing it in the mid-scale category of open-source LLMs, offering a balance between performance and resource efficiency for moderate complexity tasks. Its 4K context length falls into the short context range, making it suitable for concise interactions but limiting its ability to process extended sequences. The 13B parameter size enables robust language and vision understanding while remaining accessible for research and deployment, whereas the 4K context length ensures efficient handling of typical tasks but may require truncation for longer inputs.

• Parameter Size: 13b
• Context Length: 4k

LLaVA 13B is designed for research on large multimodal models, development of chatbots, and multimodal instruction-following tasks, with possible applications in areas like interactive learning, content generation, and cross-modal analysis. Its possible use in chatbot development could enable more dynamic and context-aware interactions, while its possible role in multimodal instruction-following might support tasks requiring integration of visual and textual data. However, these possible uses require thorough investigation to ensure alignment with specific goals and constraints. The model’s design emphasizes flexibility for research and experimental applications, but possible limitations in scalability or adaptability may arise depending on the context.

• research on large multimodal models
• development of chatbots
• multimodal instruction-following tasks

LLaVA 13B is a versatile model with possible applications in areas such as interactive educational tools, content generation for creative projects, cross-modal analysis of text and images, and experimental chatbot interactions. Its possible ability to process multimodal data could support tasks like visual question answering or instruction-following in controlled environments. However, these possible uses require careful validation to ensure they meet specific requirements and avoid unintended consequences. The model’s design prioritizes flexibility for research and experimental scenarios, but possible limitations in scalability or adaptability may emerge depending on the context.

• interactive educational tools
• content generation for creative projects
• cross-modal analysis of text and images
• experimental chatbot interactions

LLaVA 13B in its medium q4 version requires a GPU with at least 16GB VRAM (e.g., RTX 3090) and 32GB system memory for optimal performance, making it suitable for mid-scale models. Possible applications may demand additional resources depending on workload, but this configuration balances precision and efficiency for general use. Quantized versions include fp16, q2, q3, q4, q5, q6, q8.

LLaVA 13B is a mid-scale open-source model with 13B parameters and a 4K context length, designed for research on large multimodal models, chatbot development, and multimodal instruction-following tasks. Its architecture combines a vision encoder with Vicuna to enable versatile visual and language understanding, making it suitable for experimental and academic applications.

Huggingface Model Page
Ollama Model Page