Maintainer: BAAI

Total Score


Last updated 5/27/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model Overview

Bunny-Llama-3-8B-V is a family of lightweight but powerful multimodal models developed by BAAI. It offers multiple plug-and-play vision encoders, like EVA-CLIP and SigLIP, as well as language backbones including Llama-3-8B-Instruct, Phi-1.5, StableLM-2, Qwen1.5, MiniCPM, and Phi-2.

Model Inputs and Outputs

Bunny-Llama-3-8B-V is a multimodal model that can consume both text and images, and produce text outputs.


  • Text Prompt: A text prompt or instruction that the model uses to generate a response.
  • Image: An optional image that the model can use to inform its text generation.


  • Generated Text: The model's response to the provided text prompt and/or image.


The Bunny-Llama-3-8B-V model is capable of generating coherent and relevant text outputs based on a given text prompt and/or image. It can be used for a variety of multimodal tasks, such as image captioning, visual question answering, and image-grounded text generation.

What Can I Use It For?

Bunny-Llama-3-8B-V can be used for a variety of multimodal applications, such as:

  • Image Captioning: Generate descriptive captions for images.
  • Visual Question Answering: Answer questions about the contents of an image.
  • Image-Grounded Dialogue: Generate responses in a conversation that are informed by a relevant image.
  • Multimodal Content Creation: Produce text outputs that are coherently grounded in visual information.

Things to Try

Some interesting things to try with Bunny-Llama-3-8B-V could include:

  • Experimenting with different text prompts and image inputs to see how the model responds.
  • Evaluating the model's performance on standard multimodal benchmarks like VQAv2, OKVQA, and COCO Captions.
  • Exploring the model's ability to reason about and describe diagrams, charts, and other types of visual information.
  • Investigating how the model's performance varies when using different language backbones and vision encoders.

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

AI model preview image



Total Score


bunny-phi-2-siglip is a lightweight multimodal model developed by adirik, the creator of the StyleMC text-guided image generation and editing model. It is part of the Bunny family of models, which leverage a variety of vision encoders like EVA-CLIP and SigLIP, combined with language backbones such as Phi-2, Llama-3, and MiniCPM. The Bunny models are designed to be powerful yet compact, outperforming state-of-the-art large multimodal language models (MLLMs) despite their smaller size. bunny-phi-2-siglip in particular, built upon the SigLIP vision encoder and Phi-2 language model, has shown exceptional performance on various benchmarks, rivaling the capabilities of much larger 13B models like LLaVA-13B. Model inputs and outputs Inputs image**: An image in the form of a URL or image file prompt**: The text prompt to guide the model's generation or reasoning temperature**: A value between 0 and 1 that adjusts the randomness of the model's outputs, with 0 being completely deterministic and 1 being fully random top_p**: The percentage of the most likely tokens to sample from during decoding, which can be used to control the diversity of the outputs max_new_tokens**: The maximum number of new tokens to generate, with a word generally containing 2-3 tokens Outputs string**: The model's generated text response based on the input image and prompt Capabilities bunny-phi-2-siglip demonstrates impressive multimodal reasoning and generation capabilities, outperforming larger models on various benchmarks. It can handle a wide range of tasks, from visual question answering and captioning to open-ended language generation and reasoning. What can I use it for? The bunny-phi-2-siglip model can be leveraged for a variety of applications, such as: Visual Assistance**: Generating captions, answering questions, and providing detailed descriptions about images. Multimodal Chatbots**: Building conversational agents that can understand and respond to both text and images. Content Creation**: Assisting with the generation of text content, such as articles or stories, based on visual prompts. Educational Tools**: Developing interactive learning experiences that combine text and visual information. Things to try One interesting aspect of bunny-phi-2-siglip is its ability to perform well on tasks despite its relatively small size. Experimenting with different prompts, image types, and task settings can help uncover the model's nuanced capabilities and limitations. Additionally, exploring the model's performance on specialized datasets or comparing it to other similar models, such as LLaVA-13B, can provide valuable insights into its strengths and potential use cases.

Read more

Updated Invalid Date



Total Score


The cogvlm2-llama3-chat-19B model is part of the CogVLM2 series of open-source models developed by THUDM. It is based on the Meta-Llama-3-8B-Instruct model, with significant improvements in benchmarks such as TextVQA and DocVQA. The model supports up to 8K content length and 1344x1344 image resolution, and provides both English and Chinese language support. The cogvlm2-llama3-chinese-chat-19B model is a similar Chinese-English bilingual version of the same architecture. Both models are 19B in size and designed for image understanding and dialogue tasks. Model inputs and outputs Inputs Text**: The models can take text-based inputs, such as questions, instructions, or prompts. Images**: The models can also accept image inputs up to 1344x1344 resolution. Outputs Text**: The models generate text-based responses, such as answers, descriptions, or generated text. Capabilities The CogVLM2 models have achieved strong performance on a variety of benchmarks, competing with or surpassing larger non-open-source models. For example, the cogvlm2-llama3-chat-19B model scored 84.2 on TextVQA and 92.3 on DocVQA, while the cogvlm2-llama3-chinese-chat-19B model scored 85.0 on TextVQA and 780 on OCRbench. What can I use it for? The CogVLM2 models are well-suited for a variety of applications that involve image understanding and language generation, such as: Visual question answering**: Use the models to answer questions about images, diagrams, or other visual content. Image captioning**: Generate descriptive captions for images. Multimodal dialogue**: Engage in contextual conversations that reference images or other visual information. Document understanding**: Extract information and answer questions about complex documents, reports, or technical manuals. Things to try One interesting aspect of the CogVLM2 models is their ability to handle both Chinese and English inputs and outputs. This makes them useful for applications that require language understanding and generation in multiple languages, such as multilingual customer service chatbots or translation tools. Another intriguing feature is the models' high-resolution image support, which enables them to work with detailed visual content like engineering diagrams, architectural plans, or medical scans. Developers could explore using the CogVLM2 models for tasks like visual-based technical support, design review, or medical image analysis.

Read more

Updated Invalid Date




Total Score


alpaca-30b is a large language model instruction-tuned on the Tatsu Labs Alpaca dataset by Baseten. It is based on the LLaMA-30B model and was fine-tuned for 3 epochs using the Low-Rank Adaptation (LoRA) technique. The model is capable of understanding and generating human-like text in response to a wide range of instructions and prompts. Similar models include alpaca-lora-7b and alpaca-lora-30b, which are also LLaMA-based models fine-tuned on the Alpaca dataset. The llama-30b-instruct-2048 model from Upstage is another similar large language model, though it was trained on a different set of datasets. Model inputs and outputs The alpaca-30b model is designed to take in natural language instructions and generate relevant and coherent responses. The input can be a standalone instruction, or an instruction paired with additional context information. Inputs Instruction**: A natural language description of a task or query that the model should respond to. Input context (optional)**: Additional information or context that can help the model generate a more relevant response. Outputs Response**: The model's generated text response that attempts to appropriately complete the requested task or answer the given query. Capabilities The alpaca-30b model is capable of understanding and responding to a wide variety of instructions, from simple questions to more complex tasks. It can engage in open-ended conversation, provide summaries and explanations, offer suggestions and recommendations, and even tackle creative writing prompts. The model's strong language understanding and generation abilities make it a versatile tool for applications like virtual assistants, chatbots, and content generation. What can I use it for? The alpaca-30b model could be used for various applications that involve natural language processing and generation, such as: Virtual Assistants**: Integrate the model into a virtual assistant to handle user queries, provide information and recommendations, and complete task-oriented instructions. Chatbots**: Deploy the model as the conversational engine for a chatbot, allowing it to engage in open-ended dialogue and assist users with a range of inquiries. Content Generation**: Leverage the model's text generation capabilities to create original content, such as articles, stories, or even marketing copy. Research and Development**: Use the model as a starting point for further fine-tuning or as a benchmark to evaluate the performance of other language models. Things to try One interesting aspect of the alpaca-30b model is its ability to handle long-form inputs and outputs. Unlike some smaller language models, this 30B parameter model can process and generate text up to 2048 tokens in length, allowing for more detailed and nuanced responses. Experiment with providing the model with longer, more complex instructions or prompts to see how it handles more sophisticated tasks. Another intriguing feature is the model's compatibility with the LoRA (Low-Rank Adaptation) fine-tuning technique. This approach enables efficient updating of the model's parameters, making it potentially easier and more cost-effective to further fine-tune the model on custom datasets or use cases. Explore the possibilities of LoRA-based fine-tuning to adapt the alpaca-30b model to your specific needs.

Read more

Updated Invalid Date




Total Score


The llava-1.5-7b-hf model is an open-source chatbot trained by fine-tuning the LLaMA and Vicuna models on GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture, developed by llava-hf. Similar models include the llava-v1.6-mistral-7b-hf and nanoLLaVA models. The llava-v1.6-mistral-7b-hf model leverages the mistralai/Mistral-7B-Instruct-v0.2 language model and improves upon LLaVa-1.5 with increased input image resolution and an improved visual instruction tuning dataset. The nanoLLaVA model is a smaller 1B vision-language model designed to run efficiently on edge devices. Model inputs and outputs Inputs Text prompts**: The model can accept text prompts to generate responses. Images**: The model can also accept one or more images as part of the input prompt to generate captions, answer questions, or complete other multimodal tasks. Outputs Text responses**: The model generates text responses based on the input prompts and any provided images. Capabilities The llava-1.5-7b-hf model is capable of a variety of multimodal tasks, including image captioning, visual question answering, and multimodal chatbot use cases. It can generate coherent and relevant responses by combining its language understanding and visual perception capabilities. What can I use it for? You can use the llava-1.5-7b-hf model for a range of applications that require multimodal understanding and generation, such as: Intelligent assistants**: Integrate the model into a chatbot or virtual assistant to provide users with a more engaging and contextual experience by understanding and responding to both text and visual inputs. Content generation**: Use the model to generate image captions, visual descriptions, or other multimodal content to enhance your applications or services. Education and training**: Leverage the model's capabilities to develop interactive learning experiences that combine textual and visual information. Things to try One interesting aspect of the llava-1.5-7b-hf model is its ability to understand and reason about the relationship between text and images. Try providing the model with a prompt that includes both text and an image, and see how it can use the visual information to generate more informative and relevant responses.

Read more

Updated Invalid Date