BakLLaVA-1
Maintainer: SkunkworksAI
370
👀
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model Overview
BakLLaVA-1 is a large language model developed by SkunkworksAI that combines the Mistral 7B base with the LLaVA 1.5 architecture. It showcases that the Mistral 7B base outperforms the Llama 2 13B model on several benchmarks. This first version of BakLLaVA is fully open-source but was trained on data that includes the LLaVA corpus, which has licensing restrictions. An upcoming version, BakLLaVA-2, will use a larger and commercially viable dataset along with a novel architecture.
Model Inputs and Outputs
BakLLaVA-1 is a text-to-image generation model that takes in text prompts and outputs corresponding images. The model was trained on a diverse dataset of over 1 million image-text pairs from sources like LAION, CC, SBU, and ShareGPT.
Inputs
- Text prompt describing the desired image
Outputs
- Generated image based on the input text prompt
Capabilities
BakLLaVA-1 demonstrates strong text-to-image generation capabilities, outperforming the Llama 2 13B model on several benchmarks according to the maintainer. The model can generate a wide variety of images from detailed textual descriptions.
What Can I Use It For?
BakLLaVA-1 can be used for various text-to-image generation tasks, such as creating custom illustrations, generating product images, or visualizing creative ideas. The model's open-source nature and strong performance make it a potentially useful tool for researchers, artists, and developers working on visual AI applications.
Things to Try
One interesting aspect of BakLLaVA-1 is its use of the LLaVA 1.5 architecture, which combines a large language model with a vision encoder. This allows the model to efficiently leverage both textual and visual information, potentially leading to more coherent and realistic image generation. Researchers and developers may want to experiment with fine-tuning or adapting the model for their specific use cases to take advantage of these multimodal capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🐍
bakLlava-v1-hf
49
bakLlava-v1-hf is a multimodal language model derived from the original LLaVA architecture, using the Mistral-7b text backbone. It is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on a diverse dataset of image-text pairs, GPT-generated multimodal instruction-following data, academic-task-oriented VQA data, and additional private data. According to the maintainer, the model showcases that a Mistral 7B base can outperform Llama 2 13B on several benchmarks. The upcoming BakLLaVA-2 model will feature a significantly larger dataset and a novel architecture that expands beyond the current LLaVA method. Similar models include the llava-1.5-7b-hf, which uses the original LLaVA 1.5 architecture, and the BakLLaVA-1, which is a Mistral 7B base augmented with the LLaVA 1.5 architecture. Model inputs and outputs Inputs Image**: The model can take one or more images as input, which are then processed by the vision encoder. Prompt**: The model expects a multi-turn conversation prompt in the format USER: xxx\nASSISTANT:, with the token `` inserted where the image should be queried. Outputs Generated text**: The model outputs a continuation of the provided prompt, generating relevant responses based on the input image and text. Capabilities bakLlava-v1-hf demonstrates strong performance on a variety of multimodal tasks, including image captioning, visual question answering, and open-ended dialogue. The model can understand and reason about the content of images, and provide informative and engaging responses to queries. What can I use it for? You can use bakLlava-v1-hf for a wide range of multimodal AI applications, such as: Intelligent virtual assistants**: Incorporate the model into a chatbot or virtual assistant to enable natural language interactions with images. Image-based question answering**: Build applications that can answer questions about the content of images. Image captioning**: Generate descriptive captions for images to support accessibility or improve search and discovery. Things to try Experiment with different types of images and prompts to see the model's capabilities in action. Try prompting the model with open-ended questions, task-oriented instructions, or creative scenarios to explore the breadth of its knowledge and language generation abilities.
Updated Invalid Date
🧠
llava-v1.5-7B-GGUF
153
The llava-v1.5-7B-GGUF model is an open-source chatbot trained by fine-tuning the LLaMA/Vicuna language model on a diverse dataset of GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture, developed by the researcher jartine. The model was trained in September 2023 and is licensed under the LLAMA 2 Community License. Similar models include the LLaVA-13b-delta-v0, llava-v1.6-mistral-7b, llava-1.5-7b-hf, and ShareGPT4V-7B, all of which are multimodal chatbot models based on the LLaVA architecture. Model inputs and outputs Inputs Image:** The model can process and generate responses based on provided images. Text prompt:** The model takes in a text-based prompt, typically following a specific template, to generate a response. Outputs Text response:** The model generates a text-based response based on the provided image and prompt. Capabilities The llava-v1.5-7B-GGUF model is capable of performing a variety of multimodal tasks, such as image captioning, visual question answering, and instruction-following. It can generate coherent and relevant responses to prompts that involve both text and images, drawing on its training on a diverse dataset of multimodal instruction-following data. What can I use it for? The primary use of the llava-v1.5-7B-GGUF model is for research on large multimodal models and chatbots. It can be utilized by researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence to explore the capabilities and limitations of such models. Additionally, the model's ability to process and respond to multimodal prompts could be leveraged in various applications, such as chatbots, virtual assistants, and educational tools. Things to try One interesting aspect of the llava-v1.5-7B-GGUF model is its potential to combine visual and textual information in novel ways. Experimenters could try providing the model with prompts that involve both images and text, and observe how it synthesizes the information to generate relevant and coherent responses. Additionally, users could explore the model's capabilities in handling complex or ambiguous prompts, or prompts that require reasoning about the content of the image.
Updated Invalid Date
🐍
llava-v1.5-7b-llamafile
153
The llava-v1.5-7b-llamafile is an open-source chatbot model developed by Mozilla. It is trained by fine-tuning the LLaMA/Vicuna language model on a diverse dataset of multimodal instruction-following data. This model aims to push the boundaries of large language models (LLMs) by incorporating multimodal capabilities, making it a valuable resource for researchers and hobbyists working on advanced AI systems. The model is based on the transformer architecture and can be used for a variety of tasks, including language generation, question answering, and instruction-following. Similar models include the llava-v1.5-7b, llava-v1.5-13b, llava-v1.5-7B-GGUF, llava-v1.6-vicuna-7b, and llava-v1.6-34b, all of which are part of the LLaVA model family developed by researchers at Mozilla. Model inputs and outputs The llava-v1.5-7b-llamafile model is an autoregressive language model, meaning it generates text one token at a time based on the previous tokens. The model can take a variety of inputs, including text, images, and instructions, and can generate corresponding outputs, such as text, images, or actions. Inputs Text**: The model can take text inputs in the form of questions, statements, or instructions. Images**: The model can also take image inputs, which it can use to generate relevant text or to guide its actions. Instructions**: The model is designed to follow multimodal instructions, which can combine text and images to guide the model's output. Outputs Text**: The model can generate coherent and contextually relevant text, such as answers to questions, explanations, or stories. Actions**: In addition to text generation, the model can also generate actions or steps to follow instructions, such as task completion or object manipulation. Images**: While the llava-v1.5-7b-llamafile model is primarily focused on text-based tasks, it may also have some limited image generation capabilities. Capabilities The llava-v1.5-7b-llamafile model is designed to excel at multimodal tasks that involve understanding and generating both text and visual information. It can be used for a variety of applications, such as question answering, task completion, and open-ended dialogue. The model's strong performance on instruction-following benchmarks suggests that it could be particularly useful for developing advanced AI assistants or interactive applications. What can I use it for? The llava-v1.5-7b-llamafile model can be a valuable tool for researchers and hobbyists working on a wide range of AI-related projects. Some potential use cases include: Research on multimodal AI systems**: The model's ability to integrate and process both textual and visual information can be leveraged to advance research in areas such as computer vision, natural language processing, and multimodal learning. Development of interactive AI assistants**: The model's instruction-following capabilities and text generation skills make it a promising candidate for building conversational AI agents that can understand and respond to user inputs in a more natural and contextual way. Prototyping and testing of AI-powered applications**: The llava-v1.5-7b-llamafile model can be used as a starting point for building and testing various AI-powered applications, such as chatbots, task-completion tools, or virtual assistants. Things to try One interesting aspect of the llava-v1.5-7b-llamafile model is its ability to follow complex, multimodal instructions that combine text and visual information. Researchers and hobbyists could experiment with providing the model with a variety of instruction-following tasks, such as step-by-step guides for assembling furniture or recipes for cooking a meal, and observe how well the model can comprehend and execute the instructions. Another potential area of exploration is the model's text generation capabilities. Users could prompt the model with open-ended questions or topics and see how it generates coherent and contextually relevant responses. This could be particularly useful for tasks like creative writing, summarization, or text-based problem-solving. Overall, the llava-v1.5-7b-llamafile model represents an exciting step forward in the development of large, multimodal language models, and researchers and hobbyists are encouraged to explore its capabilities and potential applications.
Updated Invalid Date
bakllava
38
BakLLaVA-1 is a large language model developed by the SkunkworksAI team. It is built upon the Mistral 7B base and incorporates the LLaVA 1.5 architecture, a vision-language model. This combination allows BakLLaVA-1 to excel at both language understanding and generation, as well as visual tasks like image captioning and visual question answering. The model is similar to other vision-language models like DeepSeek-VL: An open-source Vision-Language Model and LLaVA v1.6: Large Language and Vision Assistant (Mistral-7B), which aim to combine language and vision capabilities in a single model. Model inputs and outputs BakLLaVA-1 takes two main inputs: an image and a prompt. The image can be in various formats, and the prompt is a natural language instruction or question about the image. The model then generates a textual output, which could be a description, analysis, or answer related to the input image and prompt. Inputs Image**: An input image in various formats Prompt**: A natural language instruction or question about the input image Outputs Text**: A generated textual response describing, analyzing, or answering the prompt in relation to the input image Capabilities BakLLaVA-1 is capable of a wide range of vision and language tasks, including image captioning, visual question answering, and multi-modal reasoning. It can generate detailed descriptions of images, answer questions about the contents of an image, and even perform analysis and inference based on the visual and textual inputs. What can I use it for? BakLLaVA-1 can be useful for a variety of applications, such as: Automated image captioning and description generation for social media, e-commerce, or accessibility Visual question answering for educational or assistive technology applications Multimodal content creation and generation for marketing, journalism, or creative industries Enhancing existing computer vision and natural language processing pipelines with its robust capabilities Things to try One interesting aspect of BakLLaVA-1 is its ability to perform cross-modal reasoning, where it can infer information about an image based on the prompt, or vice versa. For example, you could try providing the model with an image of a particular object and ask it to describe the object in detail, or you could give it a prompt about a scene and ask it to generate an image that matches the description.
Updated Invalid Date