Add aya (#36521)

* initial commit * small fix * move stuff to image processing file * remove stuff in validate turn and fix return tensor * remove liquid stuff * in the process of addressing comments * changes to get the right tokenization * new __init__ works * fixing defulat std and mean * works * small testing scipt -- to be deleted before merge * remove redundant code * addressing comments * fix inits, add docs templates * refactor processor, switch to gotocr image processor * remove image proc from init * refactor to working llava-style architecture * Change AyaVisionModel to AyaVisionForConditionalGeneration * add tests * fixups * update doc * Adding logits_to_keep explicitly in ayavision forward to enable compatibility with cohere model * better variable names + remove code paths * Updates to aya_vision.md * address comments * adding copied from * make style and remove unused projector_hidden_act from config * sort init * include usage of fast image proc and proc on cuda in doc * update checkpoint iin test processor * update checkpoint in test processor 2 * remove test_model and update docstring * skip failing tests --------- Co-authored-by: Saurabh Dash <saurabh@cohere.com> Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>
2025-03-04 12:24:33 +01:00
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+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# AyaVision
+
+## Overview
+
+The Aya Vision 8B and 32B models is a state-of-the-art multilingual multimodal models developed by Cohere For AI. They build on the Aya Expanse recipe to handle both visual and textual information without compromising on the strong multilingual textual performance of the original model.
+
+Aya Vision 8B combines the `Siglip2-so400-384-14` vision encoder with the Cohere CommandR-7B language model further post-trained with the Aya Expanse recipe, creating a powerful vision-language model capable of understanding images and generating text across 23 languages. Whereas, Aya Vision 32B uses Aya Expanse 32B as the language model.
+
+Key features of Aya Vision include:
+- Multimodal capabilities in 23 languages
+- Strong text-only multilingual capabilities inherited from CommandR-7B post-trained with the Aya Expanse recipe and Aya Expanse 32B
+- High-quality visual understanding using the Siglip2-so400-384-14 vision encoder
+- Seamless integration of visual and textual information in 23 languages.
+
+<!-- <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/aya_vision_architecture.webp"
+alt="drawing" width="600"/>
+
+<small> Aya Vision architecture. </small> -->
+
+Tips:
+
+- Aya Vision is a multimodal model that takes images and text as input and produces text as output.
+- Images are represented using the `<image>` tag in the templated input.
+- For best results, use the `apply_chat_template` method of the processor to format your inputs correctly.
+- The model can process multiple images in a single conversation.
+- Aya Vision can understand and generate text in 23 languages, making it suitable for multilingual multimodal applications.
+
+This model was contributed by [saurabhdash](https://huggingface.co/saurabhdash) and [yonigozlan](https://huggingface.co/yonigozlan).
+
+
+## Usage
+
+Here's how to use Aya Vision for inference:
+
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
+
+model_id = "CohereForAI/aya-vision-8b"
+torch_device = "cuda:0"
+
+# Use fast image processor
+processor = AutoProcessor.from_pretrained(model_id, use_fast=True)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id, device_map=torch_device, 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?"},
+    ]},
+    ]
+
+# Process image on CUDA
+inputs = processor.apply_chat_template(
+    messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", device=torch_device
+).to(model.device)
+
+gen_tokens = model.generate(
+    **inputs, 
+    max_new_tokens=300, 
+    do_sample=True, 
+    temperature=0.3,
+)
+
+gen_text = print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+### Pipeline
+
+```python
+from transformers import pipeline
+
+pipe = pipeline(model="CohereForAI/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)
+```
+
+### Multiple Images and Batched Inputs
+
+Aya Vision can process multiple images in a single conversation. Here's how to use it with multiple images:
+
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
+
+model_id = "CohereForAI/aya-vision-8b"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id, device_map="cuda:0", torch_dtype=torch.float16
+)
+
+# Example with multiple images in a single message
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
+            },
+            {
+                "type": "image",
+                "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
+            },
+            {
+                "type": "text",
+                "text": "These images depict two different landmarks. Can you identify them?",
+            },
+        ],
+    },
+]
+
+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,
+)
+
+gen_text = processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(gen_text)
+```
+
+For processing batched inputs (multiple conversations at once):
+
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
+
+model_id = "CohereForAI/aya-vision-8b"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id, device_map="cuda:0", torch_dtype=torch.float16
+)
+
+# Prepare two different conversations
+batch_messages = [
+    # First conversation with a single image
+    [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+                {"type": "text", "text": "Write a haiku for this image"},
+            ],
+        },
+    ],
+    # Second conversation with multiple images
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
+                },
+                {
+                    "type": "image",
+                    "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
+                },
+                {
+                    "type": "text",
+                    "text": "These images depict two different landmarks. Can you identify them?",
+                },
+            ],
+        },
+    ],
+]
+
+# Process each conversation separately and combine into a batch
+batch_inputs = processor.apply_chat_template(
+    batch_messages, 
+    padding=True, 
+    add_generation_prompt=True, 
+    tokenize=True, 
+    return_dict=True, 
+    return_tensors="pt"
+).to(model.device)
+
+# Generate responses for the batch
+batch_outputs = model.generate(
+    **batch_inputs,
+    max_new_tokens=300,
+    do_sample=True,
+    temperature=0.3,
+)
+
+# Decode the generated responses
+for i, output in enumerate(batch_outputs):
+    response = processor.tokenizer.decode(
+        output[batch_inputs.input_ids.shape[1]:], 
+        skip_special_tokens=True
+    )
+    print(f"Response {i+1}:\n{response}\n")
+```
+
+## AyaVisionProcessor
+
+[[autodoc]] AyaVisionProcessor
+
+## AyaVisionConfig
+
+[[autodoc]] AyaVisionConfig
+
+## AyaVisionForConditionalGeneration
+
+[[autodoc]] AyaVisionForConditionalGeneration
+    - forward