Aya Expanse-8b
A
Aya Expanse 8b
Overview :
Aya Expanse is an open-weight research model with advanced multilingual capabilities. It merges high-performance pre-trained models with one year of research from Cohere For AI, including data arbitrage, multilingual preference training, safety tuning, and model fusion. This powerful multilingual large language model serves 23 languages, including Arabic, Chinese (Simplified and Traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese.
Target Users :
The target audience includes researchers, developers, and enterprises that require multilingual text generation. The model's support for multiple languages makes it particularly suitable for international companies needing to handle multilingual text data, as well as academic institutions conducting cross-lingual research.
Total Visits: 29.7M
Top Region: US(17.94%)
Website Views : 55.2K
Use Cases
In a multilingual writing assistant, Aya Expanse can help users compose texts in different languages.
In a multilingual question-answer system, Aya Expanse can understand and respond to questions posed in various languages.
In a cooking application, Aya Expanse can provide cooking instructions in multiple languages.
Features
Text generation supporting 23 different languages
Autoregressive language modeling using an optimized transformer architecture
Supervised fine-tuning, preference training, and model fusion post-training
Operational with 8K context length
Available for trial on Hugging Face Space without the need to download weights
Provides detailed installation and usage guidelines for quick developer onboarding
Supports installation of the transformers library via pip and loading the model using AutoTokenizer and AutoModelForCausalLM
Offers a wealth of community-contributed example notebooks showcasing model applications across different use cases
How to Use
1. Install the transformers library: Run `pip install 'git+https://github.com/huggingface/transformers.git'` in your terminal or command prompt.
2. Import the necessary modules: In your Python code, import AutoTokenizer and AutoModelForCausalLM.
3. Load the model and tokenizer: Use the model ID 'CohereForAI/aya-expanse-8b' to load the model and tokenizer.
4. Prepare the input data: Format the user's message into a structure that the model can accept.
5. Generate text: Use the model's generate method to create text.
6. Decode the generated text: Use the tokenizer's decode method to convert the generated tokens into readable text.
7. Print or utilize the generated text: Output the generated text to the console or use it in your application.
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase