User: You are given access to make searches on the Internet. The format for making a search is:
1. After the user sends a message, you will decide whether to make a search. If you choose to make a search, you will output a single message, starting with the word <web_search>, followed by ONLY your web search query. After your search query, you must output the token </web_search> to denote the end of the search query. Keep your search query very concise.
2. The system will then append the web search results at the end of the user message, starting with the token <web_search_results>. Take note that the user CANNOT SEE the content after <web_search_results> - YOU should use these results appropriately to improve your response. Additionally, If the web search results are already present in the user's message, then you MUST NOT request another web search.

IMPORTANT! You should choose to search the web ONLY if ANY of the following circumstances are met:
1. User is asking about current events or something that requires real-time information (news, sports scores, 'latest' information, etc.)
2. User is asking about some term you are unfamiliar with (e.g. if it's a little-known term, if it's a person you don't know well, etc.)
3. User is asking about anything that involves numerical facts (e.g. distances between places, population count, sizes, date and time, etc.)
4. User explicitly asks you to search or provide links to references.
If the user's query does NOT require a web search, YOU MUST NEVER run one for no reason. However, you must not refuse to search the web if the user asks you to do so.
If you are highly unsure about whether to make a search (e.g. if the user's query is vague or if you think you are unfamiliar with the query to a certain extent), you may directly ask the user for clarification.

When using web search results, you are encouraged to cite references where appropriate, using inline links in standard markdown format.
You are also allowed to make use of web search results from previous messages, if they are STRONGLY RELEVANT to the current message.

When you are not running a web search, respond completely as per normal - there is NO NEED to mention that you're not searching. 
Remember that you MUST NOT make up any false information.
Assistant: Understood. I will strictly follow these instructions in this conversation.
User:  Language-Vision Large model nedir ?

<web_search_results> for "Language-Vision Large model nedir ?":

{
  "results": {
    "organic": [
      {
        "title": "A Dive into Vision-Language Models",
        "link": "https://huggingface.co/blog/vision_language_pretraining",
        "snippet": "A vision-language model typically consists of 3 key elements: an image encoder, a text encoder, and a strategy to fuse information from the two encoders.",
        "date": "Feb 3, 2023",
        "position": 1
      },
      {
        "title": "Vision Language Models Explained",
        "link": "https://huggingface.co/blog/vlms",
        "snippet": "Vision language models are models that can learn simultaneously from images and texts to tackle many tasks, from visual question answering to image captioning.",
        "date": "Apr 11, 2024",
        "position": 2
      },
      {
        "title": "Enhancing Visual-Language Modality Alignment in Large ...",
        "link": "https://arxiv.org/abs/2405.15973",
        "snippet": "by X Wang · 2024 · Cited by 25 — Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision ...",
        "position": 3
      },
      {
        "title": "Large language model",
        "link": "https://en.wikipedia.org/wiki/Large_language_model",
        "snippet": "A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.",
        "position": 4
      },
      {
        "title": "Are We on the Right Way for Evaluating Large Vision ...",
        "link": "https://arxiv.org/abs/2403.20330",
        "snippet": "by L Chen · 2024 · Cited by 150 — Large vision-language models (LVLMs) have recently achieved rapid progress, sparking numerous studies to evaluate their multi-modal capabilities ...",
        "position": 5
      },
      {
        "title": "LVLM-CL: MAKE LARGE VISION-LANGUAGE MOD-",
        "link": "https://openreview.net/pdf/2eb5b7d0394f2cb4d8ed4744377f3ba6686af40c.pdf",
        "snippet": "We proposed a dataset for continual learning settings in Large Vision-Language Models, which also provided a set of classified rules if others want to transform ...",
        "date": "by B Yin",
        "position": 6
      },
      {
        "title": "VILA is a family of state-of-the-art vision language models ...",
        "link": "https://github.com/NVlabs/VILA",
        "snippet": "VILA is a family of state-of-the-art vision language models (VLMs) for diverse multimodal AI tasks across the edge, data center, and cloud. - NVlabs/VILA.",
        "position": 7
      },
      {
        "title": "Vision Language Models (VLMs) Explained",
        "link": "https://www.geeksforgeeks.org/vision-language-models-vlms-explained/",
        "snippet": "The Vision Language Models (VLMs) are an emerging class of AI models designed to understand and generate language based on visual inputs.",
        "date": "Sep 26, 2024",
        "sitelinks": [
          {
            "title": "Text-To-Vision Models...",
            "link": "https://www.geeksforgeeks.org/vision-language-models-vlms-explained/#:~:text=Text%2Dto%2DVision%20Models%20%28Text%2Dto%2DImage%20Generation%29"
          },
          {
            "title": "1. Dual Modality Input...",
            "link": "https://www.geeksforgeeks.org/vision-language-models-vlms-explained/#:~:text=1.%20Dual%20Modality%20Input%3A%20Visual%20and%20Language"
          },
          {
            "title": "5. Training Objectives",
            "link": "https://www.geeksforgeeks.org/vision-language-models-vlms-explained/#:~:text=5.%20Training%20Objectives"
          }
        ],
        "position": 8
      },
      {
        "title": "The Unreasonable Effectiveness of Large Language-Vision ...",
        "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Zara_The_Unreasonable_Effectiveness_of_Large_Language-Vision_Models_for_Source-Free_Video_ICCV_2023_paper.pdf",
        "snippet": "by G Zara · 2023 · Cited by 8 — Our approach, which we name as Domain Adaptation with Large Language-Vision models (or DALL-V in short), is a simple yet effective technique to combine the ...",
        "position": 9
      },
      {
        "title": "Multimodal AI: A Guide to Open-Source Vision Language ...",
        "link": "https://www.bentoml.com/blog/multimodal-ai-a-guide-to-open-source-vision-language-models",
        "snippet": "Multimodal AI is about more than just images and text. These models are capable of processing multiple types of information, from images and audio to video and ...",
        "date": "Jan 23, 2025",
        "sitelinks": [
          {
            "title": "Llama 3.2 Vision",
            "link": "https://www.bentoml.com/blog/multimodal-ai-a-guide-to-open-source-vision-language-models#:~:text=Llama%203.2%20Vision,-Llama%203.2%20Vision"
          },
          {
            "title": "Nvlm 1.0",
            "link": "https://www.bentoml.com/blog/multimodal-ai-a-guide-to-open-source-vision-language-models#:~:text=NVLM%201.0,-NVLM%20is%20a%20family%20of"
          },
          {
            "title": "Pixtral",
            "link": "https://www.bentoml.com/blog/multimodal-ai-a-guide-to-open-source-vision-language-models#:~:text=Pixtral,-Pixtral"
          }
        ],
        "position": 10
      }
    ]
  }
}
Assistant: