Aya Expanse 32B

A multilingual large language model supporting 23 languages

CommonProductProductivitymultilingualtext generation

Aya Expanse 32B is a multilingual large language model developed by Cohere For AI, boasting 3.2 billion parameters and focusing on high-performance multilingual support. It incorporates advanced data arbitration, multilingual preference training, secure tuning, and model merging techniques to support 23 languages, including Arabic, simplified and traditional Chinese, Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese. The model's release aims to make community-based research more accessible by providing high-performance multilingual model weights for global researchers.

Supports text generation in 23 different languages
Optimized transformer architecture suitable for multilingual environments
Post-training with supervised fine-tuning
preference training
and model merging
Available for online trials through Hugging Face Space
Provides detailed usage examples and tutorials for user learning and application
Supports local deployment and usage via pip installation of the transformers library
Applicable for various use cases
including chat

\The target audience includes researchers
developers
and enterprise users
especially professionals who need to handle multilingual text generation and understanding. The multilingual support and high performance of Aya Expanse 32B make it an ideal choice for globalization projects and multilingual research.\

Multilingual writing assistant: Helps users draft articles and letters in different languages.
Chatbot: Provides multilingual chatbot services
enabling users to converse with the bot in multiple languages.
Multilingual Q&A system: Capable of understanding and answering questions in various languages
suitable for international customer service systems.

1. Install the transformers library: Run `pip install 'git+https://github.com/huggingface/transformers.git'` in the terminal or command prompt.
2. Import the model and tokenizer: In your Python code
import AutoTokenizer and AutoModelForCausalLM.
3. Load the model: Use the model ID to load the tokenizer and the model.
4. Prepare the input data: Format the user's message to match the input requirements of the model.

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