aisak-ai
/

O

@@ -1,493 +1,83 @@
 ---
-license: apache-2.0
 language:
 - en
-pipeline_tag: image-text-to-text
 tags:
 - multimodal
-library_name: transformers
 ---
-# Qwen2-VL-7B-Instruct
-## Introduction
-We're excited to unveil **Qwen2-VL**, the latest iteration of our Qwen-VL model, representing nearly a year of innovation.
-### What’s New in Qwen2-VL?
-#### Key Enhancements:
-* **SoTA understanding of images of various resolution & ratio**: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.
-* **Understanding videos of 20min+**: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.
-* **Agent that can operate your mobiles, robots, etc.**: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.
-* **Multilingual Support**: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.
-#### Model Architecture Updates:
-* **Naive Dynamic Resolution**: Unlike before, Qwen2-VL can handle arbitrary image resolutions, mapping them into a dynamic number of visual tokens, offering a more human-like visual processing experience.
-<p align="center">
-    <img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/qwen2_vl.jpg" width="80%"/>
-<p>
-* **Multimodal Rotary Position Embedding (M-ROPE)**: Decomposes positional embedding into parts to capture 1D textual, 2D visual, and 3D video positional information, enhancing its multimodal processing capabilities.
-<p align="center">
-    <img src="http://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/mrope.png" width="80%"/>
-<p>
-We have three models with 2, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2-vl/) and [GitHub](https://github.com/QwenLM/Qwen2-VL).
-## Evaluation
-### Image Benchmarks
-| Benchmark | InternVL2-8B | MiniCPM-V 2.6 | GPT-4o-mini | **Qwen2-VL-7B** |
-| :--- | :---: | :---: | :---: | :---: |
-| MMMU<sub>val</sub>  | 51.8 | 49.8 | **60**| 54.1 |
-| DocVQA<sub>test</sub>  | 91.6 | 90.8 | - | **94.5** |
-| InfoVQA<sub>test</sub>  | 74.8 | - |  - |**76.5** |
-| ChartQA<sub>test</sub>  | **83.3** | - |- | 83.0 |
-| TextVQA<sub>val</sub>  | 77.4 | 80.1 | -| **84.3** |
-| OCRBench | 794 | **852** | 785 | 845 |
-| MTVQA | - | -  | -| **26.3** |
-| RealWorldQA | 64.4 | - | - | **70.1** |
-| MME<sub>sum</sub>   | 2210.3 | **2348.4** | 2003.4| 2326.8 |
-| MMBench-EN<sub>test</sub>  | 81.7 | - | - | **83.0** |
-| MMBench-CN<sub>test</sub>  | **81.2** | - | - |  80.5 |
-| MMBench-V1.1<sub>test</sub>  | 79.4 | 78.0 | 76.0| **80.7** |
-| MMT-Bench<sub>test</sub> | - | - | - |**63.7** |
-| MMStar | **61.5** | 57.5 |  54.8 | 60.7 |
-| MMVet<sub>GPT-4-Turbo</sub>  | 54.2 | 60.0 | **66.9** | 62.0 |
-| HallBench<sub>avg</sub>  | 45.2 | 48.1 | 46.1| **50.6** |
-| MathVista<sub>testmini</sub>  | 58.3 | **60.6** | 52.4 | 58.2 |
-| MathVision  | - | -  | - | **16.3** |
-### Video Benchmarks
-| Benchmark | Internvl2-8B | LLaVA-OneVision-7B | MiniCPM-V 2.6 | **Qwen2-VL-7B** |
-| :--- | :---: | :---: | :---: | :---: |
-| MVBench | 66.4 | 56.7 | - | **67.0** |
-| PerceptionTest<sub>test</sub> | - | 57.1 | - | **62.3** |
-| EgoSchema<sub>test</sub>  | - | 60.1 | - | **66.7** |
-| Video-MME<sub>wo/w subs</sub>  | 54.0/56.9 | 58.2/-  | 60.9/63.6 | **63.3**/**69.0** |
-## Requirements
-The code of Qwen2-VL has been in the latest Hugging face transformers and we advise you to build from source with command `pip install git+https://github.com/huggingface/transformers`, or you might encounter the following error:
-```
-KeyError: 'qwen2_vl'
-```
-## Quickstart
-We offer a toolkit to help you handle various types of visual input more conveniently. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
-```bash
-pip install qwen-vl-utils
-```
-Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
-```python
-from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
-from qwen_vl_utils import process_vision_info
-# default: Load the model on the available device(s)
-model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
-)
-# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
-# model = Qwen2VLForConditionalGeneration.from_pretrained(
-#     "Qwen/Qwen2-VL-7B-Instruct",
-#     torch_dtype=torch.bfloat16,
-#     attn_implementation="flash_attention_2",
-#     device_map="auto",
-# )
-# default processer
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
-# min_pixels = 256*28*28
-# max_pixels = 1280*28*28
-# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-# Inference: Generation of the output
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-<details>
-<summary>Without qwen_vl_utils</summary>
-```python
-from PIL import Image
-import requests
-import torch
-from torchvision import io
-from typing import Dict
-from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
-# Load the model in half-precision on the available device(s)
-model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
-)
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-# Image
-url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
-image = Image.open(requests.get(url, stream=True).raw)
-conversation = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-# Preprocess the inputs
-text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
-# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n'
-inputs = processor(
-    text=[text_prompt], images=[image], padding=True, return_tensors="pt"
-)
-inputs = inputs.to("cuda")
-# Inference: Generation of the output
-output_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids = [
-    output_ids[len(input_ids) :]
-    for input_ids, output_ids in zip(inputs.input_ids, output_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
-)
-print(output_text)
-```
-</details>
-<details>
-<summary>Multi image inference</summary>
-```python
-# Messages containing multiple images and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "file:///path/to/image1.jpg"},
-            {"type": "image", "image": "file:///path/to/image2.jpg"},
-            {"type": "text", "text": "Identify the similarities between these images."},
-        ],
-    }
-]
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-# Inference
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-</details>
-<details>
-<summary>Video inference</summary>
-```python
-# Messages containing a images list as a video and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": [
-                    "file:///path/to/frame1.jpg",
-                    "file:///path/to/frame2.jpg",
-                    "file:///path/to/frame3.jpg",
-                    "file:///path/to/frame4.jpg",
-                ],
-                "fps": 1.0,
-            },
-            {"type": "text", "text": "Describe this video."},
-        ],
-    }
-]
-# Messages containing a video and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": "file:///path/to/video1.mp4",
-                "max_pixels": 360 * 420,
-                "fps": 1.0,
-            },
-            {"type": "text", "text": "Describe this video."},
-        ],
-    }
-]
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-# Inference
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-</details>
-<details>
-<summary>Batch inference</summary>
-```python
-# Sample messages for batch inference
-messages1 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "file:///path/to/image1.jpg"},
-            {"type": "image", "image": "file:///path/to/image2.jpg"},
-            {"type": "text", "text": "What are the common elements in these pictures?"},
-        ],
-    }
-]
-messages2 = [
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Who are you?"},
-]
-# Combine messages for batch processing
-messages = [messages1, messages1]
-# Preparation for batch inference
-texts = [
-    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
-    for msg in messages
-]
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=texts,
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-# Batch Inference
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_texts = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_texts)
-```
-</details>
-### More Usage Tips
-For input images, we support local files, base64, and URLs. For videos, we currently only support local files.
-```python
-# You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text.
-## Local file path
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "file:///path/to/your/image.jpg"},
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-## Image URL
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "http://path/to/your/image.jpg"},
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-## Base64 encoded image
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "data:image;base64,/9j/..."},
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-```
-#### Image Resolution for performance boost
-The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage.
-```python
-min_pixels = 256 * 28 * 28
-max_pixels = 1280 * 28 * 28
-processor = AutoProcessor.from_pretrained(
-    "Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
-)
-```
-Besides, We provide two methods for fine-grained control over the image size input to the model:
-1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels.
-2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28.
-```python
-# min_pixels and max_pixels
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "file:///path/to/your/image.jpg",
-                "resized_height": 280,
-                "resized_width": 420,
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-# resized_height and resized_width
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "file:///path/to/your/image.jpg",
-                "min_pixels": 50176,
-                "max_pixels": 50176,
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-```
-## Limitations
-While Qwen2-VL are applicable to a wide range of visual tasks, it is equally important to understand its limitations. Here are some known restrictions:
-1. Lack of Audio Support: The current model does **not comprehend audio information** within videos.
-2. Data timeliness: Our image dataset is **updated until June 2023**, and information subsequent to this date may not be covered.
-3. Constraints in Individuals and Intellectual Property (IP): The model's capacity to recognize specific individuals or IPs is limited, potentially failing to comprehensively cover all well-known personalities or brands.
-4. Limited Capacity for Complex Instruction: When faced with intricate multi-step instructions, the model's understanding and execution capabilities require enhancement.
-5. Insufficient Counting Accuracy: Particularly in complex scenes, the accuracy of object counting is not high, necessitating further improvements.
-6. Weak Spatial Reasoning Skills: Especially in 3D spaces, the model's inference of object positional relationships is inadequate, making it difficult to precisely judge the relative positions of objects.
-These limitations serve as ongoing directions for model optimization and improvement, and we are committed to continually enhancing the model's performance and scope of application.
-## Citation
-If you find our work helpful, feel free to give us a cite.
-```
-@article{Qwen2-VL,
-  title={Qwen2-VL},
-  author={Qwen team},
-  year={2024}
-}
-@article{Qwen-VL,
-  title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
-  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
-  journal={arXiv preprint arXiv:2308.12966},
-  year={2023}
-}
-```

 ---
+library_name: transformers
+pipeline_tag: other
+license: other
+license_name: all-rights-reserved
 language:
 - en
+- de
+- fr
+- es
+- pt
+- nl
+- ru
+- cs
+- pl
+- ar
+- fa
+- he
+- tr
+- ja
+- ko
+- vi
+- th
+- id
+- ms
+- my
+- km
+- lo
+- tl
+- hi
+- bn
+- ur
+base_model: aisak-ai/aisak-tvi
 tags:
+- aisak
 - multimodal
 ---
+<img src="https://i.imgur.com/FTzBiqd.png" width="150" style="margin-left: 250px;" />
+# AISAK-O (Optimum)
+**AISAK-O**, an abbreviation for Artificially Intelligent Swiss Army Knife *Optimum*, represents a significant enhancement within the AISAK ecosystem. Boasting an impressive parameter count of 8 billion, AISAK-O stands in competition with the most substantial models regarding its comprehension abilities. Despite its relatively smaller size and lower cost, it provides performance and efficiency that are on par with its more extensive counterparts. This sophisticated multimodal artificial intelligence system demonstrates exceptional proficiency in both the processing and generation of textual and visual content, thereby rendering it a highly adaptable instrument for a diverse array of applications.
+### Model Information:
+- **Model Name**: AISAK-O
+- **Version**: 1.0
+- **Specialization**: Multimodal model proficient in interpreting textual and visual input. AISAK-O excels in tasks requiring detailed analysis and synthesis of both textual and visual data.
+### Intended Use:
+- AISAK-O is meticulously engineered for applications necessitating the comprehensive analysis of textual and visual data. Its extensive capabilities render it particularly suitable for endeavors such as image captioning, visual reasoning, humorous interpretation, location identification, and cohesive content generation. The model is exceptionally adept in contexts that demand a sophisticated comprehension of multimodal information.
+- Unleash your creativity with AISAK-O—whether you're analyzing complex visual data, crafting detailed image descriptions, or enhancing multimedia content with insightful text, this model empowers you to push the boundaries of what's possible. Let your imagination take the lead and discover endless possibilities with AISAK-O.
+### Performance:
+AISAK-O exhibits exemplary performance in multimodal tasks, surpassing conventional models in its proficiency to produce and interpret content that seamlessly amalgamates text and imagery. Its sophisticated architecture guarantees elevated accuracy and contextual pertinence in its outputs.
+|**Model**|**VQA v2**|**MMBench**|**MMMU (Eval)**|
+| :--------: | :-------: | :--------: | :-------: |
+  |AISAK-O|82.0|79.3|56.1|
+  |GPT-4V|84.4|78.1|52.4|
+### Ethical Considerations:
+- **Bias Mitigation**: The AISAK team has instituted strategies aimed at rectifying potential biases. However, it is essential for users to stay informed about the possible biases embedded in the model's outputs and to utilize the model responsibly.
+- **Fair Use**: Users are strongly encouraged to utilize the AISAK-O in a manner that is both fair and ethically sound, especially in sensitive situations, in order to guarantee equitable and precise application of the model’s capabilities.
+### Deployment:
+AISAK-O is actively deployed within the AISAK ecosystem. Ongoing updates and enhancements are planned to further refine its capabilities and performance.
+### Caveats:
+- Users should verify critical decisions based on AISAK-O’s outputs, especially in high-stakes scenarios, to ensure accuracy and reliability.
+### Contact Information:
+For inquiries or additional information about AISAK, please contact the AISAK team at mandelakorilogan@gmail.com.
+**© 2024 Mandela Logan. All rights reserved.**
+No part of this model may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the copyright holder. Unauthorized use or reproduction of this model is strictly prohibited by copyright law.