llava-v1.6-34b-hf
Maintainer: llava-hf
60
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| 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 |
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Model overview
The llava-v1.6-34b-hf model is the latest version of the LLaVA chatbot, developed by the llava-hf team. It leverages the NousResearch/Nous-Hermes-2-Yi-34B large language model as its base, and has been further trained on a diverse dataset of image-text pairs and multimodal instruction-following data. Compared to the previous LLaVA-1.5 model, this version improves upon the OCR capabilities and common sense reasoning by increasing the input image resolution and using a more comprehensive training dataset.
Similar models in the LLaVA family include the llava-v1.6-mistral-7b-hf which uses the mistralai/Mistral-7B-Instruct-v0.2 model as its base, and the llava-v1.5-7b-hf which is a smaller 7B version of the original LLaVA-1.5 model.
Model inputs and outputs
The llava-v1.6-34b-hf model is a multimodal language model, capable of processing both text and image inputs. It can be used for a variety of tasks, including image captioning, visual question answering, and multimodal chatbot interactions.
Inputs
- Text Prompt: The text input that provides context and instructions for the model. This can include questions, commands, or conversational prompts.
- Image: One or more images that the model should analyze and incorporate into its response.
Outputs
- Generated Text: The model's response, which can range from a single sentence to multiple paragraphs, depending on the input prompt and the task at hand.
Capabilities
The llava-v1.6-34b-hf model excels at tasks that require understanding and reasoning about both visual and textual information. For example, it can be used to answer questions about the contents of an image, generate captions for images, or engage in multimodal conversations where the user provides both text and images.
The model's improved OCR and common sense reasoning capabilities, as compared to the previous LLaVA-1.5 version, make it well-suited for tasks that involve processing real-world visual information, such as interpreting diagrams, charts, or other complex images.
What can I use it for?
The llava-v1.6-34b-hf model is primarily intended for research purposes, as it can be used to explore the potential of large-scale multimodal language models. Potential applications include:
- Chatbots and virtual assistants: The model can be used to build chatbots and virtual assistants that can engage in natural, multimodal conversations with users.
- Automated image captioning and visual question answering: The model can be used to generate captions for images and answer questions about their contents.
- Multimodal content generation: The model can be used to generate text that is conditioned on both textual and visual inputs, such as generating product descriptions or creative writing prompts based on images.
See the model hub to explore other versions of the LLaVA model that may be better suited for your specific use case.
Things to try
One interesting aspect of the llava-v1.6-34b-hf model is its ability to handle multiple images within a single prompt. This allows you to experiment with complex multimodal reasoning tasks, where the model needs to synthesize information from several visual inputs to generate a coherent response.
Another area to explore is the model's performance on specialized tasks or datasets. While the model was trained on a broad range of multimodal data, it may excel at certain types of visual-linguistic tasks more than others. Trying the model on benchmarks or custom datasets related to your area of interest can help you understand its strengths and limitations.
Finally, the model supports various optimization techniques, such as 4-bit quantization and the use of Flash-Attention 2, which can significantly improve the inference speed and memory efficiency of the model. Experimenting with these optimizations can help you deploy the model in more resource-constrained environments, such as mobile devices or edge computing systems.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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