--- title: aya-vision-8b canonical_url: "https://www.modelscope.cn/models/CohereForAI/aya-vision-8b" md_url: "https://www.modelscope.cn/models/CohereForAI/aya-vision-8b.md" repository: CohereForAI/aya-vision-8b last_updated: 2026-01-10 license: cc-by-nc-4.0 pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - aya_vision architectures: - AyaVisionForConditionalGeneration parameters: 8.6B tensor_type: - F16 library_name: - pytorch - transformer - safetensors 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 vlm/text/emb" - "vllm 0.9.2" downloads: 1274 stars: 3 --- # aya-vision-8b > aya-vision-8b - CohereForAI 在 ModelScope 开源的模型。Model Card for Aya Vision 8B CohereForAI/aya-vision-8b 是 ModelScope 魔搭社区上的 8.6B 参数image-text-to-text模型，采用 cc-by-nc-4.0 许可，可用 deploy_task vlm/text/emb、vllm 0.9.2 部署。 - **Repository**: CohereForAI/aya-vision-8b - **License**: cc-by-nc-4.0 - **Tasks**: image-text-to-text - **Parameters**: 8.6B - **Inference backends**: deploy_task vlm/text/emb, vllm 0.9.2 - **Downloads**: 1274 - **Stars**: 3 - **Last updated**: 2026-01-10 Source: https://www.modelscope.cn/models/CohereForAI/aya-vision-8b --- # Model Card for Aya Vision 8B **Cohere Labs Aya Vision 8B** is an open weights research release of an 8-billion parameter model with advanced capabilities optimized for a variety of vision-language use cases, including OCR, captioning, visual reasoning, summarization, question answering, code, and more. It is a multilingual model trained to excel in 23 languages in vision and language. This model card corresponds to the 8-billion version of the Aya Vision model. We also released a 32-billion version which you can find [here](https://huggingface.co/CohereLabs/aya-vision-32B). - Developed by: [Cohere Labs](https://cohere.for.ai/) - Point of Contact: [Cohere Labs](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/cohere-labs-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: c4ai-aya-vision-8b - Model Size: 8 billion parameters - Context length: 16K ## Try it: Aya Vision in Action Before downloading the weights, you can try Aya Vision chat in the [Cohere playground](https://dashboard.cohere.com/playground/chat) or our dedicated [Hugging Face Space](https://huggingface.co/spaces/CohereLabs/aya_expanse) for interactive exploration. ## Example Notebook You can also check out the following [notebook](https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/aya_vision_intro.ipynb) to understand how to use Aya Vision for different use cases. ## How to Use Aya Vision Please install `transformers` from the source repository that includes the necessary changes for this model: ```python # pip install 'git+https://github.com/huggingface/transformers.git@v4.49.0-AyaVision' from transformers import AutoProcessor, AutoModelForImageTextToText import torch model_id = "CohereLabs/aya-vision-8b" processor = AutoProcessor.from_pretrained(model_id) model = AutoModelForImageTextToText.from_pretrained( model_id, device_map="auto", torch_dtype=torch.float16 ) # Format message with the aya-vision chat template messages = [ {"role": "user", "content": [ {"type": "image", "url": "https://pbs.twimg.com/media/Fx7YvfQWYAIp6rZ?format=jpg&name=medium"}, {"type": "text", "text": "चित्र में लिखा पाठ क्या कहता है?"}, ]}, ] inputs = processor.apply_chat_template( messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device) gen_tokens = model.generate( **inputs, max_new_tokens=300, do_sample=True, temperature=0.3, ) print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)) ``` You can also use the model directly using transformers `pipeline` abstraction: ```python from transformers import pipeline pipe = pipeline(model="CohereLabs/aya-vision-8b", task="image-text-to-text", device_map="auto") # Format message with the aya-vision chat template messages = [ {"role": "user", "content": [ {"type": "image", "url": "https://media.istockphoto.com/id/458012057/photo/istanbul-turkey.jpg?s=612x612&w=0&k=20&c=qogAOVvkpfUyqLUMr_XJQyq-HkACXyYUSZbKhBlPrxo="}, {"type": "text", "text": "Bu resimde hangi anıt gösterilmektedir?"}, ]}, ] outputs = pipe(text=messages, max_new_tokens=300, return_full_text=False) print(outputs) ``` ## Model Details **Input:** Model accepts input text and images. **Output:** Model generates text. **Model Architecture:** This is a vision-language model that uses a multilingual language model based on [Command R7B](https://huggingface.co/CohereLabs/c4ai-command-r7b-12-2024) and further post-trained with the [Aya Expanse recipe](https://arxiv.org/abs/2412.04261), paired with [SigLIP2-patch14-384](https://huggingface.co/google/siglip2-so400m-patch14-384) vision encoder through a multimodal adapter for vision-language understanding. **Image Processing:** We use **169 visual tokens** to encode an image tile with a resolution of **364x364 pixels**. Input images of arbitrary sizes are mapped to the nearest supported resolution based on the aspect ratio. Aya Vision uses up to 12 input tiles and a thumbnail (resized to 364x364) (2197 image tokens). **Languages covered:** The model has been trained on 23 languages: English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Chinese (Simplified and Traditional), Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian. **Context length**: Aya Vision 8B supports a context length of 16K. For more details about how the model was trained, check out [our blogpost](https://huggingface.co/blog/aya-vision). ## Evaluation We evaluated Aya Vision 8B against [Pangea 7B](https://huggingface.co/neulab/Pangea-7B), [Llama-3.2 11B Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision), [Molmo-D 7B](https://huggingface.co/allenai/Molmo-7B-D-0924), [Qwen2.5-VL 7B](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), [Pixtral 12B](https://huggingface.co/mistralai/Pixtral-12B-2409), and [Gemini Flash 1.5 8B](https://developers.googleblog.com/en/gemini-15-flash-8b-is-now-generally-available-for-use/) using [Aya Vision Benchmark](https://huggingface.co/datasets/CohereLabs/AyaVisionBench) and [m-WildVision](https://huggingface.co/datasets/CohereLabs/m-WildVision). Win-rates were determined using claude-3-7-sonnet-20250219 as a judge, based on the superior judge performance compared to other models. We also evaluated Aya Vision 8B’s performance for text-only input against the same models using [m-ArenaHard](https://huggingface.co/datasets/CohereLabs/m-ArenaHard), a challenging open-ended generation evaluation, measured using win-rates using gpt-4o-2024-11-20 as a judge. ### 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 8 billion parameter Vision-Language Model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/cohere-labs-cc-by-nc-license), requires also adhering to [Cohere Lab's Acceptable Use Policy](https://docs.cohere.com/docs/cohere-labs-acceptable-use-policy)