yalm-100b
Maintainer: yandex
122
💬
| 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
[object Object] is a large GPT-like neural network developed by Yandex. It can be used for generating and processing text, leveraging 100 billion parameters. The model was trained on a diverse corpus of 1.7 TB of online texts, books, and other sources in both English and Russian over 65 days using a cluster of 800 A100 graphics cards.
Compared to similar models like GPT-2, [object Object] is significantly larger in scale, with 100 billion parameters compared to GPT-2's 124 million. The training process was also more extensive, utilizing a much larger dataset across multiple languages. This allows [object Object] to potentially handle a wider range of text generation and processing tasks.
Model inputs and outputs
The [object Object] model takes in text as input and generates text as output. It can be used for a variety of natural language processing tasks, such as text generation, language modeling, and text understanding.
Inputs
- Text: The model accepts text input, which can be in the form of a single sentence, a paragraph, or a longer document.
Outputs
- Generated text: The model outputs generated text, which can be used for tasks like content creation, dialogue generation, and more.
Capabilities
The [object Object] model is a powerful text generation tool that can be used for a wide range of applications. Its large scale and extensive training process allow it to generate coherent and natural-sounding text on a variety of topics. The model can be particularly useful for tasks like content creation, language translation, and open-ended dialogue.
What can I use it for?
The [object Object] model can be used for a variety of natural language processing tasks, including:
- Content creation: Generate blog posts, articles, or other long-form content on a given topic.
- Language translation: Fine-tune the model for translation between English and Russian, or other language pairs.
- Dialogue generation: Use the model to create open-ended dialogues or chatbot responses.
- Text summarization: Condense long documents into concise summaries.
The model's large scale and diverse training data make it a powerful tool for researchers and developers working on natural language processing applications.
Things to try
One key aspect of the [object Object] model is its ability to generate text in both English and Russian. Developers and researchers could explore using the model for cross-lingual applications, such as building multilingual chatbots or translating content between the two languages.
Another interesting avenue to explore would be fine-tuning the model on specific datasets or tasks, such as scientific writing or customer service dialogues. This could help the model develop specialized knowledge and capabilities tailored to particular domains or use cases.
Overall, the [object Object] model represents an impressive advancement in large language model technology, and there are many exciting possibilities for how it could be leveraged in real-world applications.
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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