Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained Command family of models with the recently released Aya Collection. The result is a powerful multilingual large language model serving 23 languages.
This model card corresponds to the 8-billion version of the Aya 23 model. We also released a 35-billion version which you can find here.
You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space here.
Usage
Please install transformers from the source repository that includes the necessary changes for this model
python
1# pip install transformers==4.41.12from transformers import AutoTokenizer, AutoModelForCausalLM
34model_id ="CohereForAI/aya-23-8B"5tokenizer = AutoTokenizer.from_pretrained(model_id)6model = AutoModelForCausalLM.from_pretrained(model_id)78# Format message with the command-r-plus chat template9messages =[{"role":"user","content":"Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}]10input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")11## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>1213gen_tokens = model.generate(14 input_ids,15 max_new_tokens=100,16 do_sample=True,17 temperature=0.3,18)1920gen_text = tokenizer.decode(gen_tokens[0])21print(gen_text)
Example Notebook
This notebook showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with QLoRA.
Model Details
Input: Models input text only.
Output: Models generate text only.
Model Architecture: Aya-23-8B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions.
Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese
Context length: 8192
Evaluation
multilingual benchmarks
average win rates
Please refer to the Aya 23 technical report for further details about the base model, data, instruction tuning, and evaluation.
Model Card Contact
For errors or additional questions about details in this model card, contact info@for.ai.
Terms of Use
We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to C4AI's Acceptable Use Policy.
Try the model today
You can try Aya 23 in the Cohere playground here. You can also use it in our dedicated Hugging Face Space here.
Citation info
bibtex
1@misc{aya23technicalreport,
2 title={Aya 23: Open Weight Releases to Further Multilingual Progress},
3 author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker},
4 url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23},
5 year={2024}
6}
7
