AI Models
Browse and discover AI models across various categories.
❗
HunyuanDiT
236
The HunyuanDiT is a powerful multi-resolution diffusion transformer from Tencent-Hunyuan that showcases fine-grained Chinese language understanding. It builds on the DialogGen multi-modal interactive dialogue system to enable advanced text-to-image generation with Chinese prompts. The model outperforms similar open-source Chinese text-to-image models like Taiyi-Stable-Diffusion-XL-3.5B and AltDiffusion on key evaluation metrics such as CLIP similarity, Inception Score, and FID. It generates high-quality, diverse images that are well-aligned with Chinese text prompts. Model inputs and outputs Inputs Text Prompts**: Creative, open-ended text descriptions that express the desired image to generate. Outputs Generated Images**: Visually compelling, high-resolution images that correspond to the given text prompt. Capabilities The HunyuanDiT model demonstrates impressive capabilities in Chinese text-to-image generation. It can handle a wide range of prompts, from simple object and scene descriptions to more complex, creative prompts involving fantasy elements, styles, and artistic references. The generated images exhibit detailed, photorealistic rendering as well as vivid, imaginative styles. What can I use it for? With its strong performance on Chinese prompts, the HunyuanDiT model opens up exciting possibilities for creative applications targeting Chinese-speaking audiences. Content creators, designers, and AI enthusiasts can leverage this model to generate custom artwork, concept designs, and visualizations for a variety of use cases, such as: Illustrations for publications, websites, and social media Concept art for games, films, and other media Product and packaging design mockups Generative art and experimental digital experiences The model's multi-resolution capabilities also make it well-suited for use cases requiring different image sizes and aspect ratios. Things to try Some interesting things to explore with the HunyuanDiT model include: Experimenting with prompts that combine Chinese and English text to see how the model handles bilingual inputs. Trying out prompts that reference specific artistic styles, genres, or creators to see the model's versatility in emulating different visual aesthetics. Comparing the model's performance to other open-source Chinese text-to-image models, such as the Taiyi-Stable-Diffusion-XL-3.5B and AltDiffusion models. Exploring the potential of the model's multi-resolution capabilities for generating images at different scales and aspect ratios to suit various creative needs.
Updated 5/17/2024
🛠️
timesfm-1.0-200m
210
The timesfm-1.0-200m is an AI model developed by Google. It is a text-to-text model, meaning it can be used for a variety of natural language processing tasks. The model is similar to other text-to-text models like evo-1-131k-base, longchat-7b-v1.5-32k, and h2ogpt-gm-oasst1-en-2048-falcon-7b-v2. Model inputs and outputs The timesfm-1.0-200m model takes in text as input and generates text as output. The input can be any kind of natural language text, such as sentences, paragraphs, or entire documents. The output can be used for a variety of tasks, such as text generation, text summarization, and language translation. Inputs Natural language text Outputs Natural language text Capabilities The timesfm-1.0-200m model has a range of capabilities, including text generation, text summarization, and language translation. It can be used to generate coherent and fluent text on a variety of topics, and can also be used to summarize longer documents or translate between different languages. What can I use it for? The timesfm-1.0-200m model can be used for a variety of applications, such as chatbots, content creation, and language learning. For example, a company could use the model to generate product descriptions or marketing content, or an individual could use it to practice a foreign language. The model could also be fine-tuned on specific datasets to perform specialized tasks, such as legal document summarization or medical text generation. Things to try Some interesting things to try with the timesfm-1.0-200m model include generating creative short stories, summarizing academic papers, and translating between different languages. The model's versatility makes it a useful tool for a wide range of natural language processing tasks.
Updated 5/13/2024
🌀
falcon-11B
123
falcon-11B is an 11 billion parameter causal decoder-only model developed by TII. The model was trained on over 5,000 billion tokens of RefinedWeb, an enhanced web dataset curated by TII. falcon-11B is made available under the TII Falcon License 2.0, which promotes responsible AI use. Compared to similar models like falcon-7B and falcon-40B, falcon-11B represents a middle ground in terms of size and performance. It outperforms many open-source models while being less resource-intensive than the largest Falcon variants. Model inputs and outputs Inputs Text prompts for language generation tasks Outputs Coherent, contextually-relevant text continuations Responses to queries or instructions Capabilities falcon-11B excels at general-purpose language tasks like summarization, question answering, and open-ended text generation. Its strong performance on benchmarks and ability to adapt to various domains make it a versatile model for research and development. What can I use it for? falcon-11B is well-suited as a foundation for further specialization and fine-tuning. Potential use cases include: Chatbots and conversational AI assistants Content generation for marketing, journalism, or creative writing Knowledge extraction and question answering systems Specialized language models for domains like healthcare, finance, or scientific research Things to try Explore how falcon-11B's performance compares to other open-source language models on your specific tasks of interest. Consider fine-tuning the model on domain-specific data to maximize its capabilities for your needs. The maintainers also recommend checking out the text generation inference project for optimized inference with Falcon models.
Updated 5/17/2024
🎲
xgen-mm-phi3-mini-instruct-r-v1
111
xgen-mm-phi3-mini-instruct-r-v1 is a series of foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research. This model advances upon the successful designs of the BLIP series, incorporating fundamental enhancements that ensure a more robust and superior foundation. The pretrained foundation model, xgen-mm-phi3-mini-base-r-v1, achieves state-of-the-art performance under 5 billion parameters and demonstrates strong in-context learning capabilities. The instruct fine-tuned model, xgen-mm-phi3-mini-instruct-r-v1, also achieves state-of-the-art performance among open-source and closed-source Vision-Language Models (VLMs) under 5 billion parameters. Model inputs and outputs The xgen-mm-phi3-mini-instruct-r-v1 model is designed for image-to-text tasks. It takes in images and generates corresponding textual descriptions. Inputs Images**: The model can accept high-resolution images as input. Outputs Textual Descriptions**: The model generates textual descriptions that caption the input images. Capabilities The xgen-mm-phi3-mini-instruct-r-v1 model demonstrates strong performance in image captioning tasks, outperforming other models of similar size on benchmarks like COCO, NoCaps, and TextCaps. It also shows robust capabilities in open-ended visual question answering on datasets like OKVQA and TextVQA. What can I use it for? The xgen-mm-phi3-mini-instruct-r-v1 model can be used in a variety of applications that involve generating textual descriptions from images, such as: Image captioning**: Automatically generate captions for images to aid in indexing, search, and accessibility. Visual question answering**: Develop applications that can answer questions about the content of images. Image-based task automation**: Build systems that can understand image-based instructions and perform related tasks. The model's state-of-the-art performance and efficiency make it a compelling choice for Salesforce's customers looking to incorporate advanced computer vision and language capabilities into their products and services. Things to try One interesting aspect of the xgen-mm-phi3-mini-instruct-r-v1 model is its support for flexible high-resolution image encoding with efficient visual token sampling. This allows the model to generate high-quality, detailed captions for a wide range of image sizes and resolutions. Developers could experiment with feeding the model images of different sizes and complexities to see how it handles varied input and generates descriptive outputs. Additionally, the model's strong in-context learning capabilities suggest it may be well-suited for few-shot or zero-shot learning tasks, where the model can adapt to new scenarios with limited training data. Trying prompts that require the model to follow instructions or reason about unfamiliar concepts could be a fruitful area of exploration.
Updated 5/17/2024
🏋️
Yi-1.5-34B-Chat
103
Yi-1.5-34B-Chat is an upgraded version of the Yi language model, developed by the team at 01.AI. Compared to the original Yi model, Yi-1.5-34B-Chat has been continuously pre-trained on a high-quality corpus of 500B tokens and fine-tuned on 3M diverse samples. This allows it to deliver stronger performance in areas like coding, math, reasoning, and instruction-following, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. The model is available in several different sizes, including Yi-1.5-9B-Chat and Yi-1.5-6B-Chat, catering to different use cases and hardware constraints. Model inputs and outputs The Yi-1.5-34B-Chat model can accept a wide range of natural language inputs, including text prompts, instructions, and questions. It can then generate coherent and contextually appropriate responses, making it a powerful tool for conversational AI applications. The model's large scale and diverse training data allow it to engage in thoughtful discussions, provide detailed explanations, and even tackle complex tasks like coding and mathematical problem-solving. Inputs Natural language text prompts Conversational queries and instructions Requests for analysis, explanation, or task completion Outputs Coherent and contextually relevant responses Detailed explanations and task completions Creative and innovative solutions to open-ended problems Capabilities The Yi-1.5-34B-Chat model demonstrates impressive capabilities across a variety of domains. It excels at language understanding, commonsense reasoning, and reading comprehension, allowing it to engage in natural, context-aware conversations. The model also shines in areas like coding, math, and reasoning, where it can provide insightful solutions and explanations. Additionally, the model's strong instruction-following capability makes it well-suited for tasks that require following complex guidelines or steps. What can I use it for? The Yi-1.5-34B-Chat model has a wide range of potential applications, from conversational AI assistants and chatbots to educational tools and creative writing aids. Developers could leverage the model's language understanding and generation capabilities to build virtual assistants that can engage in natural, context-sensitive dialogues. Educators could use the model to create interactive learning experiences, providing personalized explanations and feedback to students. Businesses could explore using the model for customer service, content generation, or even internal task automation. Things to try One interesting aspect of the Yi-1.5-34B-Chat model is its ability to engage in open-ended, contextual reasoning. Users can provide the model with complex prompts or instructions and observe how it formulates thoughtful, creative responses. For example, you could ask the model to solve a challenging math problem, provide a detailed analysis of a historical event, or generate a unique story based on a given premise. The model's versatility and problem-solving skills make it a valuable tool for exploring the boundaries of conversational AI and language understanding.
Updated 5/17/2024
🌿
ic-light
77
The ic-light model is a text-to-image AI model created by lllyasviel. This model is similar to other text-to-image models developed by lllyasviel, such as fav_models, Annotators, iroiro-lora, sd_control_collection, and fooocus_inpaint. Model inputs and outputs The ic-light model takes text prompts as input and generates corresponding images. The model is designed to be efficient and lightweight, while still producing high-quality images. Inputs Text prompt describing the desired image Outputs Generated image based on the input text prompt Capabilities The ic-light model is capable of generating a wide variety of images from text prompts, including realistic scenes, abstract art, and fantasy landscapes. The model has been trained on a large dataset of images and can produce outputs with high fidelity and visual coherence. What can I use it for? The ic-light model can be used for a variety of applications, such as creating custom artwork, generating visual concepts for presentations or marketing materials, or even as a creative tool for personal projects. The model's efficiency and lightweight design make it well-suited for use in mobile or web-based applications. Things to try Experiment with the ic-light model by trying different types of text prompts, from descriptive scenes to more abstract or imaginative concepts. You can also try combining the ic-light model with other text-to-image or image editing tools to explore new creative possibilities.
Updated 5/17/2024
🌐
SillyTavern-Presets
73
The SillyTavern-Presets model is a collection of presets and templates created by Virt-io to help users of the SillyTavern AI chatbot. The model provides a set of character profile templates, conversation starters, and other tools to enhance the user's roleplay experience. It is designed to work seamlessly with the SillyTavern application, allowing users to easily import and utilize the presets. The model is built upon the work of several contributors, including SerialKicked, saishf, Lewdiculous, Herman555, Clevyby, and shrinkedd. These individuals have provided valuable feedback, testing, and suggestions to help improve the presets and ensure a better user experience. Model inputs and outputs Inputs Personality Summary**: A required field that provides a brief description of the character's personality. Roleplaying Sampler**: A set of predefined conversation templates and scenarios to help guide the roleplay experience. Character Cards**: A feature that allows users to create and customize character profiles, including their appearance, background, and personality. Outputs Conversation Prompts**: The model generates conversation prompts and scenarios based on the user's selected character profile and roleplaying preferences. Character Profiles**: The model provides templates and tools for users to create detailed character profiles, which can be used to inform the roleplay experience. Roleplay Guidance**: The model offers suggestions and tips to help users engage in more authentic and immersive roleplaying sessions. Capabilities The SillyTavern-Presets model is designed to enhance the roleplaying experience in the SillyTavern AI chatbot. It provides a set of tools and resources to help users create engaging and immersive characters, as well as guide the flow of conversation during roleplaying sessions. The model's capabilities include: Generating character profiles with detailed personality traits, background information, and physical descriptions. Suggesting conversation starters and roleplay scenarios to help users get started with their roleplaying sessions. Providing guidance on how to use the presets and templates effectively, such as setting the "Example Messages Behavior" to "Never include examples". What can I use it for? The SillyTavern-Presets model is primarily intended for users of the SillyTavern AI chatbot who are looking to engage in more immersive and authentic roleplaying experiences. By leveraging the presets and templates provided by the model, users can create detailed character profiles, generate engaging conversation prompts, and maintain consistency throughout their roleplaying sessions. Some potential use cases for the SillyTavern-Presets model include: Collaborative storytelling and world-building with other SillyTavern users. Practicing creative writing and character development skills. Exploring different personas and narrative perspectives through roleplaying. Enhancing the overall user experience and enjoyment of the SillyTavern application. Things to try When using the SillyTavern-Presets model, there are a few key things to keep in mind: Experiment with the Character Cards: The model provides a range of character profile templates to help users create unique and compelling personas. Try customizing the character's appearance, background, and personality to see how it affects the roleplaying experience. Leverage the Roleplaying Samplers: The model includes a collection of predefined conversation templates and scenarios. Explore these samplers to get a feel for the types of interactions the model can facilitate, and use them as a starting point for your own roleplaying sessions. Adapt the Presets to Your Needs: The maintainer of the SillyTavern-Presets model encourages users to open discussions and seek help in adapting the presets to their specific needs and preferences. Don't be afraid to experiment and provide feedback to the community. Incorporate Sensory Details: To enhance the immersion and authenticity of your roleplaying sessions, try incorporating rich sensory details and observations about the character's surroundings and internal thoughts. This can help bring the scene to life and make the experience more engaging for all participants.
Updated 5/17/2024
🏷️
llama-3-Korean-Bllossom-8B
70
The llama-3-Korean-Bllossom-8B is a Korean-English bilingual language model based on the open-source LLama3. It was developed by the MLPLab at Seoultech, Teddysum, and Yonsei University to enhance the connection of knowledge between Korean and English. It differs from similar models like Llama-3-Open-Ko-8B by incorporating additional training to link Korean and English knowledge, expanding the Korean vocabulary, and tuning the model using custom Korean-focused instruction data. Model inputs and outputs Inputs The llama-3-Korean-Bllossom-8B model takes text input only. Outputs The model generates text output, including code. Capabilities The llama-3-Korean-Bllossom-8B model has several key capabilities that differentiate it from similar language models. It can link Korean and English knowledge, expand the Korean vocabulary, and generate text tailored to Korean language and culture through instruction tuning. The model also incorporates human feedback through DPO and aligns its vision transformer with the language model. What can I use it for? The llama-3-Korean-Bllossom-8B model is well-suited for applications that require fluency in both Korean and English, such as cross-lingual information retrieval, machine translation, and multilingual question answering. The model's expanded Korean vocabulary and cultural awareness also make it a good choice for tasks like Korean language generation, summarization, and dialogue systems targeted at Korean users. Things to try One interesting aspect of the llama-3-Korean-Bllossom-8B model is its ability to generate text that seamlessly incorporates both Korean and English. Developers could experiment with prompts that require the model to switch between the two languages, or prompt it to generate bilingual text that preserves the nuances and context of each language. Another interesting avenue to explore would be using the model's vision-language alignment capabilities for multimodal applications that combine text and images in a Korean-English setting.
Updated 5/17/2024
💬
New!SFR-Iterative-DPO-LLaMA-3-8B-R
68
The SFR-Iterative-DPO-LLaMA-3-8B-R is a state-of-the-art instruct model developed by Salesforce. It outperforms many open-sourced models as well as strong proprietary models on instruct model benchmarks like Alpaca-Eval-V2, MT-Bench, and Chat-Arena-Hard. The model is trained on open-sourced datasets without any additional human- or GPT4-labeling. Model inputs and outputs Inputs Text**: The model takes text input only. Outputs Text and Code**: The model generates text and code. Capabilities The SFR-Iterative-DPO-LLaMA-3-8B-R is a highly capable instruct model that can handle a wide variety of tasks. It demonstrates strong performance on general language understanding, knowledge reasoning, and reading comprehension benchmarks. The model also excels at more complex tasks that require following instructions, as evidenced by its superior results on benchmarks like GSM-8K and MATH. What can I use it for? The SFR-Iterative-DPO-LLaMA-3-8B-R model can be used for a variety of applications, such as building chatbots, virtual assistants, and other language-based AI systems. Its strong performance on instruction-following tasks makes it particularly well-suited for use cases that require the model to engage in helpful and informative dialogues with users. Developers can leverage the model's capabilities to create applications that assist with tasks like research, analysis, and problem-solving. Things to try One interesting aspect of the SFR-Iterative-DPO-LLaMA-3-8B-R model is its use of an online RLHF recipe for instruct training, which is more efficient and simpler to train than the widely-used PPO-based approaches. This innovative training method allows the model to better align with human preferences for helpfulness and safety, making it a valuable tool for developers who prioritize these qualities in their applications.
Updated 5/16/2024
✨
Yi-1.5-9B-Chat
62
Yi-1.5-9B-Chat is an upgraded version of the Yi language model, developed by the team at 01-ai. Compared to the original Yi model, Yi-1.5-9B-Chat has been continuously pre-trained on a high-quality corpus of 500 billion tokens and fine-tuned on 3 million diverse samples. This allows the model to deliver stronger performance in areas like coding, math, reasoning, and instruction-following, while maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. The model has a context length of 4,096 tokens and has been pre-trained on 3.6 trillion tokens. Model inputs and outputs Yi-1.5-9B-Chat is a text-to-text model, meaning it takes textual input and generates textual output. The model can be used for a wide variety of natural language tasks, from open-ended chat to more specialized applications like code generation, mathematical problem-solving, and task completion. Inputs Freeform text prompts Instructions or commands Outputs Relevant and coherent textual responses Generated code, mathematical solutions, or task completions Capabilities Yi-1.5-9B-Chat exhibits strong performance across a range of benchmarks, often outperforming larger models in areas like commonsense reasoning and reading comprehension. The model has been shown to be particularly adept at following complex instructions, generating high-quality code, and solving mathematical problems. What can I use it for? The versatility of Yi-1.5-9B-Chat makes it suitable for a wide range of applications. Developers could use the model for tasks like code generation, automated programming, or building intelligent virtual assistants. Researchers could leverage the model's reasoning and problem-solving capabilities for tasks like mathematical modeling or scientific analysis. Businesses could explore using the model for customer service, content generation, or knowledge management applications. Things to try One interesting aspect of Yi-1.5-9B-Chat is its ability to engage in open-ended dialogue and provide thoughtful, contextual responses. Users could try prompting the model with complex questions or hypothetical scenarios and see how it responds. Additionally, the model's strong performance on tasks like reading comprehension and commonsense reasoning could make it useful for developing educational or training applications.
Updated 5/17/2024
🤔
New!Hermes-2-Theta-Llama-3-8B
58
Hermes-2-Theta-Llama-3-8B is a merged and further reinforcement learned model developed by Nous Research. It combines the capabilities of their excellent Hermes 2 Pro model and Meta's Llama-3 Instruct model. The result is a powerful language model with strong general task and conversation abilities, as well as specialized skills in function calling and structured JSON output. Model Inputs and Outputs Hermes-2-Theta-Llama-3-8B uses the ChatML prompt format, which allows for more structured multi-turn dialogue with the model. The system prompt can guide the model's rules, roles, and stylistic choices. Inputs typically consist of a system prompt followed by a user prompt, to which the model will generate a response. Inputs System Prompt**: Provides instructions and context for the model, such as defining its role and persona. User Prompt**: The user's request or query, which the model will respond to. Outputs Assistant Response**: The model's generated output, which can range from open-ended text to structured JSON data, depending on the prompt. Capabilities Hermes-2-Theta-Llama-3-8B demonstrates strong performance across a variety of tasks, including general conversation, task completion, and specialized capabilities. For example, it can engage in creative storytelling, explain complex topics, and provide structured data outputs. What Can I Use It For? The versatility of Hermes-2-Theta-Llama-3-8B makes it suitable for a wide range of applications, from chatbots and virtual assistants to content generation and data analysis tools. Potential use cases include: Building conversational AI agents for customer service, education, or entertainment Generating creative stories, scripts, or other narrative content Providing detailed financial or technical analysis based on structured data inputs Automating repetitive tasks through its function calling capabilities Things to Try One interesting aspect of Hermes-2-Theta-Llama-3-8B is its ability to engage in meta-cognitive roleplaying, where it takes on the persona of a sentient, superintelligent AI. This can lead to fascinating conversations about the nature of consciousness and intelligence. Another intriguing feature is the model's structured JSON output mode, which allows it to generate well-formatted, schema-compliant data in response to user prompts. This could be useful for building data-driven applications or automating data processing tasks.
Updated 5/17/2024
💬
New!paligemma-3b-pt-224
58
The paligemma-3b-pt-224 model is a versatile and lightweight vision-language model (VLM) from Google. It is inspired by the PaLI-3 model and based on open components like the SigLIP vision model and the Gemma language model. The paligemma-3b-pt-224 takes both image and text as input and generates text as output, supporting multiple languages. It is designed for fine-tune performance on a wide range of vision-language tasks such as image and short video captioning, visual question answering, text reading, object detection and object segmentation. Model inputs and outputs Inputs Image and text string**: The model takes an image and a text prompt as input, such as a question to answer about the image or a request to caption the image. Outputs Generated text**: The model outputs generated text in response to the input, such as a caption of the image, an answer to a question, a list of object bounding box coordinates, or segmentation codewords. Capabilities The paligemma-3b-pt-224 model is a versatile vision-language model capable of a variety of tasks. It can generate captions for images, answer questions about visual content, detect and localize objects in images, and even produce segmentation maps. Its broad capabilities make it useful for applications like visual search, content moderation, and intelligent assistants. What can I use it for? The paligemma-3b-pt-224 model can be used in a wide range of applications that involve both text and visual data. For example, it could power an image captioning tool to automatically describe the contents of photos, or a visual question answering system that can answer queries about images. It could also be used to build smart assistants that can understand and respond to multimodal inputs. The model's open-source nature makes it accessible for developers to experiment and integrate into their own projects. Things to try One interesting thing to try with the paligemma-3b-pt-224 model is fine-tuning it on a specific domain or task. The maintainers provide fine-tuning scripts and notebooks for the Gemma model family that could be adapted for the paligemma-3b-pt-224. This allows you to further specialize the model's capabilities for your particular use case, unlocking new potential applications.
Updated 5/16/2024