Xtuner
Models by this creator
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llava-llama-3-8b-v1_1-gguf
109
llava-llama-3-8b-v1_1 is a LLaVA model fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner. It is similar to the llava-llama-3-8b-v1_1 model, which is also an XTuner LLaVA model fine-tuned from the same base models. The key difference is that this llava-llama-3-8b-v1_1-gguf model is in GGUF format, whereas the other is in XTuner LLaVA format. Model inputs and outputs Inputs Text prompts Images (for multimodal tasks) Outputs Generated text Answers to prompts Image captions and descriptions Capabilities The llava-llama-3-8b-v1_1-gguf model is capable of performing a variety of text-to-text and text-to-image generation tasks. It can engage in open-ended dialogue, answer questions, summarize text, and generate creative content. The model has also been fine-tuned for multimodal tasks, allowing it to describe images, answer visual questions, and generate images based on text prompts. What can I use it for? You can use llava-llama-3-8b-v1_1-gguf for a wide range of applications, such as building chatbots, virtual assistants, content creation tools, and multimodal AI systems. The model's strong performance on benchmarks suggests it could be a valuable tool for research, education, and commercial applications that require language and vision capabilities. Things to try One interesting thing to try with this model is exploring its multimodal capabilities. You can provide it with images and see how it responds, or generate images based on text prompts. Another interesting aspect to explore is the model's language understanding and generation abilities, which could be useful for tasks like question answering, summarization, and creative writing.
Updated 6/5/2024
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llava-llama-3-8b-v1_1
105
llava-llama-3-8b-v1_1 is a LLaVA model fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner. This model is in XTuner LLaVA format. Model inputs and outputs Inputs Text prompts Images Outputs Text responses Image captions Capabilities The llava-llama-3-8b-v1_1 model is capable of multimodal tasks like image captioning, visual question answering, and multimodal conversations. It performs well on benchmarks like MMBench, CCBench, and SEED-IMG, demonstrating strong visual understanding and reasoning capabilities. What can I use it for? You can use llava-llama-3-8b-v1_1 for a variety of multimodal applications, such as: Intelligent virtual assistants that can understand and respond to text and images Automated image captioning and visual question answering tools Educational applications that combine text and visual content Chatbots with the ability to understand and reference visual information Things to try Try using llava-llama-3-8b-v1_1 to generate captions for images, answer questions about the content of images, or engage in multimodal conversations where you can reference visual information. Experiment with different prompting techniques and observe how the model responds.
Updated 5/28/2024
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llava-phi-3-mini-gguf
84
llava-phi-3-mini is a LLaVA model fine-tuned from microsoft/Phi-3-mini-4k-instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner. This LLaVA model is similar to other fine-tuned LLaVA models like llava-llama-3-8b-v1_1 and Phi-3-mini-4k-instruct-gguf, but has been further optimized by XTuner. Model inputs and outputs Inputs Text**: The model takes textual prompts as input. Outputs Text**: The model generates relevant text responses to the input prompts. Capabilities The llava-phi-3-mini model is capable of engaging in open-ended conversations, answering questions, and generating human-like text on a wide range of topics. It has been fine-tuned to follow instructions and exhibit traits like helpfulness, safety, and truthfulness. What can I use it for? The llava-phi-3-mini model can be used for research and commercial applications that require a capable language model, such as building chatbots, virtual assistants, or text generation tools. Given its fine-tuning on instructional datasets, it may be particularly well-suited for applications that involve task-oriented dialogue or text generation based on user prompts. Things to try Some interesting things to try with llava-phi-3-mini include: Engaging the model in open-ended conversations on a wide range of topics to see its natural language abilities. Providing it with step-by-step instructions or prompts to see how it can break down and complete complex tasks. Exploring its reasoning and problem-solving skills by giving it math, logic, or coding problems to solve. Assessing its safety and truthfulness by trying to prompt it to generate harmful or false content. The versatility of this LLaVA model means there are many possibilities for experimentation and discovery.
Updated 6/4/2024