--- title: aya-23-8B canonical_url: "https://www.modelscope.cn/models/CohereForAI/aya-23-8B" md_url: "https://www.modelscope.cn/models/CohereForAI/aya-23-8B.md" repository: CohereForAI/aya-23-8B last_updated: 2025-09-11 license: cc-by-nc-4.0 pipeline_tag: text-generation tasks: - text-generation model_type: - cohere architectures: - CohereForCausalLM parameters: 8.0B tensor_type: - F16 library_name: - transformer - safetensors - pytorch frameworks: - pytorch language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi inference_backends: - "deploy_task text/emb" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 1070 stars: 1 --- # aya-23-8B > aya-23-8B - CohereForAI 在 ModelScope 开源的模型。Model Card for Aya-23-8B CohereForAI/aya-23-8B 是 ModelScope 魔搭社区上的 8.0B 参数text-generation模型，采用 cc-by-nc-4.0 许可，可用 deploy_task text/emb、sglang 0.5.2、vllm 0.9.2 部署。 - **Repository**: CohereForAI/aya-23-8B - **License**: cc-by-nc-4.0 - **Tasks**: text-generation - **Parameters**: 8.0B - **Inference backends**: deploy_task text/emb, sglang 0.5.2, vllm 0.9.2 - **Downloads**: 1070 - **Stars**: 1 - **Last updated**: 2025-09-11 Source: https://www.modelscope.cn/models/CohereForAI/aya-23-8B --- # Model Card for Aya-23-8B **Note:** This is an older version of Aya. The latest version is Aya Expanse 8B which is available [here](https://huggingface.co/CohereLabs/aya-expanse-8b). We also have multimodal variant, Aya Vision 8B which is available [here](https://huggingface.co/CohereLabs/aya-vision-8b). **Try Aya Expanse and Aya Vision:** You can try out latest Aya models before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereLabs/aya_expanse). ## Model Summary 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](https://huggingface.co/CohereLabs/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereLabs/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](https://huggingface.co/CohereLabs/aya-23-35B). We cover 23 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 Developed by: [Cohere Labs](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere Labs: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [Cohere Lab's Acceptable Use Policy](https://docs.cohere.com/docs/cohere-labs-acceptable-use-policy) - Model: aya-23-8B - Model Size: 8 billion parameters ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install transformers==4.41.1 from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereLabs/aya-23-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <|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|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereLabs/aya-23-8B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## 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](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) 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 labs@cohere.com ### 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](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [Cohere Lab's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereLabs/aya-23). ### Citation info ```bibtex @misc{aryabumi2024aya, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi and John Dang and Dwarak Talupuru and Saurabh Dash and David Cairuz and Hangyu Lin and Bharat Venkitesh and Madeline Smith and Kelly Marchisio and Sebastian Ruder and Acyr Locatelli and Julia Kreutzer and Nick Frosst and Phil Blunsom and Marzieh Fadaee and Ahmet Üstün and Sara Hooker}, year={2024}, eprint={2405.15032}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```