Google

Models by this creator

🧠

gemma-7b

google

Total Score

2.8K

gemma-7b is a 7B parameter version of the Gemma family of lightweight, state-of-the-art open models from Google. Gemma models are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. These models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. The relatively small size of Gemma models makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state-of-the-art AI models. The Gemma family also includes the gemma-2b, gemma-7b-it, and gemma-2b-it models, which offer different parameter sizes and instruction-tuning options. Model inputs and outputs Inputs Text string**: The model takes a text string as input, such as a question, a prompt, or a document to be summarized. Outputs Generated text**: The model generates English-language text in response to the input, such as an answer to a question or a summary of a document. Capabilities The gemma-7b model is capable of a wide range of text generation tasks, including question answering, summarization, and reasoning. It can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. The model can also power conversational interfaces for chatbots and virtual assistants, as well as support interactive language learning experiences. What can I use it for? The gemma-7b model can be used for a variety of applications across different industries and domains. For example, you could use it to: Generate personalized content for marketing campaigns Build conversational AI assistants to help with customer service Summarize long documents or research papers Assist language learners by providing feedback and writing practice The model's relatively small size and open availability make it accessible for a wide range of developers and researchers, helping to democratize access to state-of-the-art AI capabilities. Things to try One interesting aspect of the gemma-7b model is its ability to handle long-form text generation. Unlike some language models that struggle with coherence and consistency over long sequences, the Gemma models are designed to maintain high-quality output even when generating lengthy passages of text. You could try using the model to generate extended narratives, such as short stories or creative writing pieces, and see how it performs in terms of maintaining a cohesive plot, character development, and logical flow. Additionally, the model's strong performance on tasks like summarization and question answering could make it a valuable tool for academic and research applications, such as helping to synthesize insights from large bodies of technical literature.

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Updated 4/29/2024

📉

flan-t5-xxl

google

Total Score

1.1K

The flan-t5-xxl is a large language model developed by Google that builds upon the T5 transformer architecture. It is part of the FLAN family of models, which have been fine-tuned on over 1,000 additional tasks compared to the original T5 models, spanning a wide range of languages including English, German, French, and many others. As noted in the research paper, the FLAN-T5 models achieve strong few-shot performance, even compared to much larger models like PaLM 62B. The flan-t5-xxl is the extra-extra-large variant of the FLAN-T5 model, with over 10 billion parameters. Compared to similar models like the Falcon-40B and FalconLite, the FLAN-T5 models focus more on being a general-purpose language model that can excel at a wide variety of text-to-text tasks, rather than being optimized for specific use cases. Model inputs and outputs Inputs Text**: The flan-t5-xxl model takes text inputs that can be used for a wide range of natural language processing tasks, such as translation, summarization, question answering, and more. Outputs Text**: The model outputs generated text, with the length and content depending on the specific task. For example, it can generate translated text, summaries, or answers to questions. Capabilities The flan-t5-xxl model is a powerful general-purpose language model that can be applied to a wide variety of text-to-text tasks. It has been fine-tuned on a massive amount of data and can perform well on tasks like question answering, summarization, and translation, even in a few-shot or zero-shot setting. The model's multilingual capabilities also make it useful for working with text in different languages. What can I use it for? The flan-t5-xxl model can be used for a wide range of natural language processing applications, such as: Translation**: Translate text between supported languages, such as English, German, and French. Summarization**: Generate concise summaries of longer text passages. Question Answering**: Answer questions based on provided context. Dialogue Generation**: Generate human-like responses in a conversational setting. Text Generation**: Produce coherent and contextually relevant text on a given topic. These are just a few examples - the model's broad capabilities make it a versatile tool for working with text data in a variety of domains and applications. Things to try One key aspect of the flan-t5-xxl model is its strong few-shot and zero-shot performance, as highlighted in the research paper. This means that the model can often perform well on new tasks with only a small amount of training data, or even without any task-specific fine-tuning. To explore this capability, you could try using the model for a range of text-to-text tasks, and see how it performs with just a few examples or no fine-tuning at all. This could help you identify areas where the model excels, as well as potential limitations or biases to be aware of. Another interesting thing to try would be to compare the performance of the flan-t5-xxl model to other large language models, such as the Falcon-40B or FalconLite, on specific tasks or benchmarks. This could provide insights into the relative strengths and weaknesses of each model, and help you choose the best tool for your particular use case.

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Updated 5/28/2024

gemma-7b-it

google

Total Score

1.1K

The gemma-7b-it model is a 7 billion parameter version of the Gemma language model, an open and lightweight model developed by Google. The Gemma model family is built on the same research and technology as Google's Gemini models, and is well-suited for a variety of text generation tasks like question answering, summarization, and reasoning. The 7B instruct version has been further tuned for instruction following, making it useful for applications that require natural language understanding and generation. The Gemma models are available in different sizes, including a 2B base model, a 7B base model, and a 2B instruct model in addition to the gemma-7b-it model. These models are designed to be deployable on resource-constrained environments like laptops and desktops, democratizing access to state-of-the-art language models. Model Inputs and Outputs Inputs Natural language text that the model will generate a response for Outputs Generated natural language text that responds to or continues the input Capabilities The gemma-7b-it model is capable of a wide range of text generation tasks, including question answering, summarization, and open-ended dialogue. It has been trained to follow instructions and can assist with tasks like research, analysis, and creative writing. The model's relatively small size allows it to be deployed on local infrastructure, making it accessible for individual developers and smaller organizations. What Can I Use It For? The gemma-7b-it model can be used for a variety of applications that require natural language understanding and generation, such as: Question answering systems to provide information and answers to user queries Summarization tools to condense long-form text into concise summaries Chatbots and virtual assistants for open-ended dialogue and task completion Writing assistants to help with research, analysis, and creative projects The model's instruction-following capabilities also make it useful for building applications that allow users to interact with the AI through natural language commands. Things to Try Here are some ideas for interesting things to try with the gemma-7b-it model: Use the model to generate creative writing prompts and short stories Experiment with the model's ability to follow complex instructions and break them down into actionable steps Finetune the model on domain-specific data to create a specialized assistant for your field of interest Explore the model's reasoning and analytical capabilities by asking it to summarize research papers or provide insights on data Remember to check the Responsible Generative AI Toolkit for guidance on using the model ethically and safely.

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Updated 4/28/2024

📶

flan-t5-base

google

Total Score

694

flan-t5-base is a language model developed by Google that is part of the FLAN-T5 family. It is an improved version of the original T5 model, with additional fine-tuning on over 1,000 tasks covering a variety of languages. Compared to the original T5 model, FLAN-T5 models like flan-t5-base are better at a wide range of tasks, including question answering, reasoning, and few-shot learning. The model is available in a range of sizes, from the base flan-t5-base to the much larger flan-t5-xxl. Similar FLAN-T5 models include flan-t5-xxl, which is a larger version of the model with better performance on some benchmarks. The Falcon series of models from TII, like Falcon-40B and Falcon-180B, are also strong open-source language models that can be used for similar tasks. Model inputs and outputs Inputs Text**: The flan-t5-base model takes text input, which can be in the form of a single sentence, a paragraph, or even longer documents. Outputs Text**: The model generates text output, which can be used for a variety of tasks such as translation, summarization, question answering, and more. Capabilities The flan-t5-base model is a powerful text-to-text transformer that can be used for a wide range of natural language processing tasks. It has shown strong performance on benchmarks like MMLU, HellaSwag, PIQA, and others, often outperforming even much larger language models. The model's versatility and few-shot learning capabilities make it a valuable tool for researchers and developers working on a variety of NLP applications. What can I use it for? The flan-t5-base model can be used for a variety of natural language processing tasks, including: Content Creation and Communication**: The model can be used to generate creative text, power chatbots and virtual assistants, and produce text summaries. Research and Education**: Researchers can use the model as a foundation for experimenting with NLP techniques, developing new algorithms, and contributing to the advancement of the field. Educators can also leverage the model to create interactive language learning experiences. Things to try One interesting aspect of the flan-t5-base model is its strong few-shot learning capabilities. This means that the model can often perform well on new tasks with just a few examples, without requiring extensive fine-tuning. Developers and researchers can experiment with prompting the model with different task descriptions and a small number of examples to see how it performs on a variety of downstream applications. Another area to explore is the model's multilingual capabilities. The flan-t5-base model is trained on over 100 languages, which opens up opportunities to use it for cross-lingual tasks like machine translation, multilingual question answering, and more.

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Updated 5/28/2024

🎲

gemma-2b

google

Total Score

675

The gemma-2b model is a lightweight, state-of-the-art open model from Google, built from the same research and technology used to create the Gemini models. It is part of the Gemma family of text-to-text, decoder-only large language models available in English, with open weights, pre-trained variants, and instruction-tuned variants. The Gemma 7B base model, Gemma 7B instruct model, and Gemma 2B instruct model are other variants in the Gemma family. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state-of-the-art AI models and helping foster innovation. Model inputs and outputs The gemma-2b model is a text-to-text, decoder-only large language model. It takes text as input and generates English-language text in response, such as answers to questions, summaries of documents, or other types of generated content. Inputs Text strings, such as questions, prompts, or documents to be summarized Outputs Generated English-language text in response to the input, such as answers, summaries, or other types of generated content Capabilities The gemma-2b model excels at a variety of text generation tasks. It can be used to generate creative content like poems, scripts, and marketing copy. It can also power conversational interfaces for chatbots and virtual assistants, or provide text summarization capabilities. The model has demonstrated strong performance on benchmarks evaluating tasks like question answering, common sense reasoning, and code generation. What can I use it for? The gemma-2b model can be leveraged for a wide range of natural language processing applications. For content creation, you could use it to draft blog posts, emails, or other written materials. In the education and research domains, it could assist with language learning tools, knowledge exploration, and advancing natural language processing research. Developers could integrate the model into chatbots, virtual assistants, and other conversational AI applications. Things to try One interesting aspect of the gemma-2b model is its relatively small size compared to larger language models, yet it still maintains state-of-the-art performance on many benchmarks. This makes it well-suited for deployment in resource-constrained environments like edge devices or personal computers. You could experiment with using the model to generate content on your local machine or explore its capabilities for tasks like code generation or common sense reasoning. The model's open weights and well-documented usage examples also make it an appealing choice for researchers and developers looking to experiment with and build upon large language model technologies.

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Updated 4/28/2024

🛠️

timesfm-1.0-200m

google

Total Score

576

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.

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Updated 6/11/2024

⚙️

vit-base-patch16-224

google

Total Score

552

The vit-base-patch16-224 is a Vision Transformer (ViT) model pre-trained on ImageNet-21k, a large dataset of 14 million images across 21,843 classes. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. The weights were later converted from the timm repository by Ross Wightman. The vit-base-patch16-224-in21k model is another ViT model pre-trained on the larger ImageNet-21k dataset, but not fine-tuned on the smaller ImageNet 2012 dataset like the vit-base-patch16-224 model. Both models use a transformer encoder architecture to process images as sequences of fixed-size patches, with the addition of a [CLS] token for classification tasks. The all-mpnet-base-v2 is a sentence-transformer model that maps sentences and paragraphs to a 768-dimensional dense vector space, enabling tasks like clustering and semantic search. It was fine-tuned on over 1 billion sentence pairs using a self-supervised contrastive learning objective. The owlvit-base-patch32 model is designed for zero-shot and open-vocabulary object detection, allowing it to detect objects without relying on pre-defined class labels. The stable-diffusion-x4-upscaler is a text-guided latent diffusion model trained for 1.25M steps on high-resolution images (>2048x2048) from the LAION dataset. It can be used to upscale low-resolution images by 4x while preserving semantic information. Model inputs and outputs Inputs Images**: The vit-base-patch16-224 and vit-base-patch16-224-in21k models take images as input, which are divided into fixed-size patches and linearly embedded. Sentences/Paragraphs**: The all-mpnet-base-v2 model takes sentences or paragraphs as input and encodes them into a dense vector representation. Low-resolution images and text prompts**: The stable-diffusion-x4-upscaler model takes low-resolution images and text prompts as input, and generates a high-resolution upscaled image. Outputs Image classification logits**: The vit-base-patch16-224 and vit-base-patch16-224-in21k models output logits for each of the 1,000 ImageNet classes. Sentence embeddings**: The all-mpnet-base-v2 model outputs a 768-dimensional vector representation for each input sentence or paragraph. High-resolution upscaled images**: The stable-diffusion-x4-upscaler model generates a high-resolution (4x) upscaled image based on the input low-resolution image and text prompt. Capabilities The vit-base-patch16-224 and vit-base-patch16-224-in21k models are capable of classifying images into 1,000 ImageNet classes with high accuracy. The all-mpnet-base-v2 model can be used for a variety of sentence-level tasks, such as information retrieval, clustering, and semantic search. The stable-diffusion-x4-upscaler model can generate high-resolution images from low-resolution inputs while preserving semantic information. What can I use it for? The vit-base-patch16-224 and vit-base-patch16-224-in21k models can be used for image classification tasks, such as recognizing objects, scenes, or activities in images. The all-mpnet-base-v2 model can be used to build applications that require semantic understanding of text, such as chatbots, search engines, or recommendation systems. The stable-diffusion-x4-upscaler model can be used to generate high-quality images for use in creative applications, design, or visualization. Things to try With the vit-base-patch16-224 and vit-base-patch16-224-in21k models, you can try fine-tuning them on your own image classification datasets to adapt them to your specific needs. The all-mpnet-base-v2 model can be used as a starting point for training your own sentence embedding models, or to generate sentence-level features for downstream tasks. The stable-diffusion-x4-upscaler model can be combined with text-to-image generation models to create high-resolution images from text prompts, opening up new possibilities for creative applications.

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Updated 5/28/2024

flan-ul2

google

Total Score

545

flan-ul2 is an encoder-decoder model based on the T5 architecture, developed by Google. It uses the same configuration as the earlier UL2 model, but with some key improvements. Unlike the original UL2 model which had a receptive field of only 512, flan-ul2 has a receptive field of 2048, making it more suitable for few-shot in-context learning tasks. Additionally, the flan-ul2 checkpoint does not require the use of mode switch tokens, which were previously necessary to achieve good performance. The flan-ul2 model was fine-tuned using the "Flan" prompt tuning approach and a curated dataset. This process aimed to improve the model's few-shot abilities compared to the original UL2 model. Similar models include the flan-t5-xxl and flan-t5-base models, which were also fine-tuned on a broad range of tasks. Model inputs and outputs Inputs Text**: The model accepts natural language text as input, which can be in the form of a single sentence, a paragraph, or a longer passage. Outputs Text**: The model generates natural language text as output, which can be used for tasks such as language translation, summarization, question answering, and more. Capabilities The flan-ul2 model is capable of a wide range of text-to-text tasks, including translation, summarization, and question answering. Its improved receptive field and removal of mode switch tokens make it better suited for few-shot learning compared to the original UL2 model. What can I use it for? The flan-ul2 model can be used as a foundation for various natural language processing applications, such as building chatbots, content generation tools, and personalized language assistants. Its few-shot learning capabilities make it a promising candidate for research into in-context learning and zero-shot task generalization. Things to try Experiment with using the flan-ul2 model for few-shot learning tasks, where you provide the model with a small number of examples to guide its understanding of a new task or problem. Additionally, you could fine-tune the model on a specific domain or dataset to further enhance its performance for your particular use case.

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Updated 5/28/2024

🔍

gemma-2b-it

google

Total Score

502

The gemma-2b-it is an instruct-tuned version of the Gemma 2B language model from Google. Gemma is a family of open, state-of-the-art models designed for versatile text generation tasks like question answering, summarization, and reasoning. The 2B instruct model builds on the base Gemma 2B model with additional fine-tuning to improve its ability to follow instructions and generate coherent text in response to prompts. Similar models in the Gemma family include the Gemma 2B base model, the Gemma 7B base model, and the Gemma 7B instruct model. These models share the same underlying architecture and training approach, but differ in scale and the addition of the instruct-tuning step. Model Inputs and Outputs Inputs Text prompts or instructions that the model should generate content in response to, such as questions, writing tasks, or open-ended requests. Outputs Generated English-language text that responds to the input prompt or instruction, such as an answer to a question, a summary of a document, or creative writing. Capabilities The gemma-2b-it model is capable of generating high-quality text output across a variety of tasks. For example, it can answer questions, write creative stories, summarize documents, and explain complex topics. The model's performance has been evaluated on a range of benchmarks, showing strong results compared to other open models of similar size. What Can I Use it For? The gemma-2b-it model is well-suited for a wide range of natural language processing applications: Content Creation**: Use the model to generate draft text for marketing copy, scripts, emails, or other creative writing tasks. Conversational AI**: Integrate the model into chatbots or virtual assistants to power more natural and engaging conversations. Research and Education**: Leverage the model as a foundation for further NLP research or to create interactive learning tools. By providing a high-performance yet accessible open model, Google hopes to democratize access to state-of-the-art language AI and foster innovation across many domains. Things to Try One interesting aspect of the gemma-2b-it model is its ability to follow instructions and generate text that aligns with specific prompts or objectives. You could experiment with giving the model detailed instructions or multi-step tasks and observe how it responds. For example, try asking it to write a short story about a specific theme, or have it summarize a research paper in a concise way. The model's flexibility and coherence in these types of guided tasks is a key strength. Another area to explore is the model's performance on more technical or specialized language, such as code generation, mathematical reasoning, or scientific writing. The diverse training data used for Gemma models is designed to expose them to a wide range of linguistic styles and domains, so they may be able to handle these types of inputs more effectively than some other language models.

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Updated 4/28/2024

flan-t5-large

google

Total Score

462

The flan-t5-large model is a large language model developed by Google and released through Hugging Face. It is an improvement upon the popular T5 model, with enhanced performance on a wide range of tasks and languages. Compared to the base T5 model, flan-t5-large has been fine-tuned on over 1,000 additional tasks, covering a broader set of languages including English, Spanish, Japanese, French, and many others. This fine-tuning process, known as "instruction finetuning", helps the model achieve state-of-the-art performance on benchmarks like MMLU. The flan-t5-xxl and flan-t5-base models are similar, larger and smaller variants of the flan-t5-large model, respectively. These models follow the same architectural improvements and fine-tuning process, but with different parameter sizes. The flan-ul2 model is another related model, built by TII, that uses a unified training approach to achieve strong performance across a variety of tasks. Model inputs and outputs Inputs Text**: The flan-t5-large model accepts text as input, which can be in the form of a single sequence or paired sequences (e.g., for tasks like translation or question answering). Outputs Text**: The model generates text as output, which can be used for a variety of natural language processing tasks such as summarization, translation, and question answering. Capabilities The flan-t5-large model excels at a wide range of natural language processing tasks, including text generation, question answering, summarization, and translation. Its performance is significantly improved compared to the base T5 model, thanks to the extensive fine-tuning on a diverse set of tasks and languages. For example, the research paper reports that the flan-t5-xxl model achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. What can I use it for? The flan-t5-large model is well-suited for research on language models, including exploring zero-shot and few-shot learning on various NLP tasks. It can also be used as a foundation for further specialization and fine-tuning on specific use cases, such as chatbots, content generation, and question answering systems. The paper suggests that the model should not be used directly in any application without a prior assessment of safety and fairness concerns. Things to try One interesting aspect of the flan-t5-large model is its ability to handle a diverse set of languages, including English, Spanish, Japanese, and many others. Researchers and developers can explore the model's performance on cross-lingual tasks, such as translating between these languages or building multilingual applications. Additionally, the model's strong few-shot learning capabilities can be leveraged to quickly adapt it to new domains or tasks with limited fine-tuning data.

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Updated 5/27/2024