qwen-vl-chat
Maintainer: lucataco
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Model overview
qwen-vl-chat is a multimodal language model developed by lucataco, a creator featured on AIModels.fyi. It is trained using alignment techniques to support flexible interaction, such as multi-round question answering, and creative capabilities. qwen-vl-chat is similar to other large language models created by lucataco, including qwen1.5-72b, qwen1.5-110b, llama-2-7b-chat, llama-2-13b-chat, and qwen-14b-chat.
Model inputs and outputs
qwen-vl-chat takes two inputs: an image and a prompt. The image is used to provide visual context, while the prompt is a question or instruction for the model to respond to.
Inputs
- Image: The input image, which can be any image format.
- Prompt: A question or instruction for the model to respond to.
Outputs
- Output: The model's response to the input prompt, based on the provided image.
Capabilities
qwen-vl-chat is a powerful multimodal language model that can engage in flexible, creative interactions. It can answer questions, generate text, and provide insights based on the input image and prompt. The model's alignment training allows it to provide responses that are aligned with the user's intent and the visual context.
What can I use it for?
qwen-vl-chat can be used for a variety of tasks, such as visual question answering, image captioning, and creative writing. For example, you could use it to describe the contents of an image, answer questions about a scene, or generate a short story inspired by a visual prompt. The model's versatility makes it a valuable tool for a range of applications, from education and entertainment to research and development.
Things to try
One interesting thing to try with qwen-vl-chat is to use it for multi-round question answering. By providing a series of follow-up questions or prompts, you can engage the model in an interactive dialogue and see how it builds upon its understanding of the visual and textual context. This can reveal the model's reasoning capabilities and its ability to maintain coherence and context over multiple exchanges.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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qwen1.5-72b is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. It was created by lucataco. Similar models include the qwen1.5-110b, whisperspeech-small, phi-3-mini-4k-instruct, moondream2, and deepseek-vl-7b-base, all of which were also developed by lucataco. Model inputs and outputs qwen1.5-72b is a language model that generates text based on a given prompt. The model takes several inputs, including the prompt, system prompt, temperature, top-k and top-p sampling parameters, repetition penalty, max new tokens, and a random seed. Inputs Prompt**: The input text that the model will use to generate additional text. System Prompt**: An optional prompt to set the overall behavior and personality of the model. Temperature**: Controls the randomness of the generated text, with higher values leading to more diverse and unpredictable outputs. Top K**: The number of most likely tokens to consider during sampling. Top P**: The cumulative probability threshold to use for nucleus sampling, which focuses the sampling on the most likely tokens. Repetition Penalty**: A penalty applied to tokens that have already been generated, to discourage repetition. Max New Tokens**: The maximum number of new tokens to generate. Seed**: A random seed value to ensure reproducible results. Outputs The model outputs an array of generated text, which can be concatenated to form a coherent response. Capabilities qwen1.5-72b is a powerful language model capable of generating human-like text on a wide range of topics. It can be used for tasks such as text completion, language generation, and dialogue systems. The model's performance can be tuned by adjusting the input parameters, allowing users to generate outputs that are more or less creative, coherent, and diverse. What can I use it for? qwen1.5-72b can be used in a variety of applications, such as: Chatbots and virtual assistants Content generation for websites, blogs, and social media Creative writing and story generation Language translation and summarization Educational and research applications The model's lightweight and efficient design also makes it suitable for deployment on edge devices, enabling on-device language processing capabilities. Things to try One interesting aspect of qwen1.5-72b is its ability to generate diverse and creative outputs by adjusting the temperature parameter. By experimenting with different temperature values, users can explore the model's range of capabilities, from more logical and coherent responses to more imaginative and unpredictable outputs. Additionally, the model's system prompt feature allows users to tailor the model's personality and behavior to suit their specific needs, opening up a wide range of potential applications.
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qwen1.5-110b
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llama-2-13b-chat
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