Qwen3 VL 2B Instruct Alternatives

Qwen2.5-VL-3B-Instruct is an open-source multimodal language model designed for both text and image understanding. It is suitable for developers and researchers building applications that require processing of multiple data types.

Qwen3-VL-8B-Instruct is an open-source multimodal language model capable of processing both text and image inputs. It supports instruction following and fine-tuning, making it ideal for AI researchers and developers building advanced multimodal AI systems.

Qwen2-VL-2B-Instruct is an open-source multimodal language model developed by Alibaba Cloud. It supports both text and image inputs for tasks such as instruction following, image understanding, and text generation. The model is suitable for researchers and developers building advanced multimodal AI applications.

Qwen3-VL-4B-Instruct is an open-source multimodal transformer model that processes both text and images. It is designed for developers and researchers building AI applications requiring integrated text and image understanding or generation.

Qwen2.5-VL-7B-Instruct is an open-source multimodal language model from Qwen, supporting both text and image understanding. It is designed for developers and researchers building AI systems that require processing and generating multimodal content.

Qwen/Qwen3-VL-235B-A22B-Instruct is a large, open-source multimodal model supporting text, image, audio, and video understanding and generation. It is designed for AI researchers and developers seeking to build or experiment with advanced multimodal AI systems.

Qwen3-VL-32B Instruct is an open-source large multimodal AI model designed for vision and language tasks. It supports instruction following and can process both text and images, making it suitable for researchers and developers building advanced multimodal applications.

Qwen2-VL-7B-Instruct-AWQ is an open-source, instruction-tuned multimodal language model capable of processing both text and images. It is designed for developers and researchers working on advanced multimodal AI applications.

Qwen3-VL-8B-Instruct-FP8 is an open-source multimodal language model capable of processing both text and images. It is designed for AI researchers and developers who need advanced capabilities in multimodal understanding, instruction following, and content generation. The model supports local and cloud deployment.

A quantized version of the Qwen2.5-VL-3B-Instruct model optimized with AWQ. It accepts both image and video inputs along with text and follows natural language instructions for vision-language tasks. The model is hosted on Hugging Face and can be loaded via the transformers library or run with inference engines supporting GGUF/AWQ formats.

Qwen3-VL-4B-Instruct-FP8 is a compact, quantized version of Alibaba's Qwen3 vision-language model. It supports image and text inputs, tool calling, and follows a chat template optimized for instruction following. The FP8 quantization makes it suitable for deployment on a wide range of devices.

Qwen2.5-VL-7B-Instruct-AWQ is an open-source multimodal large language model capable of processing both text and image inputs. It is designed for developers and researchers building advanced AI systems that require understanding of multiple data types.

Qwen2.5-3B-Instruct is a 3-billion-parameter instruction-tuned language model published on Hugging Face by the Qwen team. It forms part of the Qwen2.5 series of foundation models and is provided for download and local use. The model includes a chat template that defines its default system prompt as "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." The template supports multi-turn conversations and supplies explicit formatting for tool use. When tools are supplied, the prompt instructs the model to call one or more functions by emitting structured XML blocks containing a JSON object with function name and arguments. This mechanism allows the model to request external assistance while following a defined XML-based call format. The model is distributed as open-source weights on the Hugging Face repository. No pricing, licensing terms, or usage restrictions are stated on the page. It is delivered as downloadable model files that can be loaded with standard Hugging Face libraries for inference on compatible hardware.

Qwen3-VL-2B-Instruct-GGUF is a GGUF-quantized version of Alibaba's Qwen vision-language model. It enables local multimodal inference combining text and vision inputs for instruction following and chat. The model is designed for developers who want to run compact vision-language models on consumer hardware using standard GGUF runtimes.

Qwen2.5-VL-72B-Instruct is an open-source, large-scale foundation model capable of understanding and generating text, images, and videos. It is designed for AI researchers and developers building advanced multimodal applications and can be deployed locally or via API.

This is an AWQ 4-bit quantized version of the Qwen3-VL-2B-Instruct model, optimized for efficient inference while maintaining strong performance on vision and language tasks. It supports tool calling, multimodal inputs, and follows a specific chat template for instruction following. The model is suitable for deployment in resource-constrained environments.

Qwen3-VL-32B-Instruct-AWQ is an AWQ-quantized version of the Qwen3 vision-language model optimized for lower VRAM usage. It supports multimodal inputs combining text and images and includes a chat template with built-in tool calling support. The model is designed for developers building applications that require visual reasoning and function calling in a single efficient inference pass.

Qwen2.5-7B-Instruct is an open-source large language model developed by Alibaba Cloud, designed for instruction following and general AI tasks. It can be self-hosted or accessed via API, and is suitable for developers and researchers building AI applications or conducting experiments.

unsloth/Qwen3-VL-4B-Instruct-GGUF is a quantized 4B parameter vision-language model from the Qwen3 family. It supports image understanding, visual reasoning, and instruction following. The GGUF format enables efficient local inference on consumer hardware using compatible runtimes.

Qwen3-4B-Instruct-2507 is an open-source, instruction-tuned language model designed for efficient text generation and conversational AI. Developed by Alibaba Cloud, it offers a smaller footprint for resource-constrained environments while supporting custom fine-tuning and multi-turn dialogue. Ideal for developers seeking a compact LLM.

Qwen2.5-VL-72B-Instruct is a 72 billion parameter vision-language model developed by the Qwen team at Alibaba. It processes images, videos, and text together, supporting advanced multimodal reasoning and instruction following. The model is openly available on Hugging Face for local or hosted inference.

This is a quantized version of the Qwen3-VL-4B vision-language model optimized with AWQ 4-bit precision. It enables efficient multimodal inference combining vision and text understanding. The model supports instruction following and tool calling, making it suitable for developers building local multimodal applications with lower hardware requirements.

Qwen3-30B-A3B-Instruct-2507 is a large-scale, instruction-tuned language model designed for advanced text generation and comprehension. It is intended for developers and researchers seeking high-quality, open-source LLMs for various NLP applications.

Qwen2.5-7B-Instruct-AWQ is an instruction-tuned language model hosted on Hugging Face. It is provided in AWQ quantized format for efficient deployment and forms one variant within the Qwen2.5 model family. The model follows a specific chat template that begins with a system prompt identifying it as Qwen, created by Alibaba Cloud, and positions it as a helpful assistant. When tools are supplied, the template instructs the model to call functions by emitting structured JSON objects wrapped in XML-style tool_call tags. It supports multi-turn conversation handling through a sequence of messages that can include an optional initial system role. Delivery occurs via the Hugging Face platform, where the repository makes the model weights available for download and integration into inference pipelines. The page includes example Jinja-based chat template code that developers can use to format inputs correctly for text generation tasks. No pricing, licensing terms, or additional platform support details appear in the repository excerpt.

Qwen2-1.5B-Instruct is an open-source, instruction-tuned language model designed for text generation and conversational AI. Developed by Alibaba Cloud, it supports integration into various NLP and chatbot applications, offering open weights and flexible deployment.

This is a 4B parameter quantized version of the Qwen3-VL vision-language model optimized for MLX. It supports multimodal inputs combining text and images and can be used for visual question answering, document understanding, and tool-calling tasks. The model is distributed on Hugging Face for local inference via libraries such as Transformers or MLX.

Qwen3-VL-30B-A3B-Instruct-GGUF is a GGUF-quantized version of Alibaba's Qwen3 vision-language model. It supports image understanding, tool calling, and instruction following. The model is targeted at developers building offline multimodal applications or running large vision models locally using tools like llama.cpp.

Qwen2.5-0.5B-Instruct is an open-source, instruction-tuned language model designed for text generation and understanding. It is ideal for AI developers and researchers seeking a smaller, locally deployable LLM for various NLP tasks.

Qwen2.5-14B-Instruct is an instruction-tuned language model hosted on Hugging Face. It forms part of the Qwen2.5 series and is distributed with open weights. The model is positioned within the class of foundation models and is made available for download and use from the repository Qwen/Qwen2.5-14B-Instruct. The provided page content includes a system prompt template that defines its default behavior. It identifies the model as Qwen, created by Alibaba Cloud, and instructs it to act as a helpful assistant. The template supports conversation handling with an optional initial system message. When tools are supplied, the prompt instructs the model to consider calling one or more functions by returning structured JSON objects wrapped in XML-style tags. This mechanism enables the model to receive tool signatures in a designated XML block and to format its calls accordingly. The page is hosted by Hugging Face, an organization focused on advancing artificial intelligence through open source and open science. The content consists primarily of template code for formatting messages and tool interactions rather than a full model card or feature list.

This is an AWQ 4-bit quantized version of the Qwen3-VL-30B-A3B-Instruct multimodal model. It combines vision and language capabilities for tasks involving images and text, supporting tool use and structured output. The quantization enables efficient inference on more accessible hardware while preserving most of the original model's performance.

This repository contains GGUF quantized versions of the Qwen3-VL-8B-Instruct model, enabling efficient local inference on CPUs and GPUs. It supports vision-language tasks including image understanding, visual question answering, and document analysis. The GGUF format makes it compatible with popular local LLM runners like llama.cpp.

Qwen/Qwen3-Omni-30B-A3B-Instruct is an open-source, instruction-tuned large language model designed for advanced text generation and AI research. It supports integration via API or CLI and is suitable for developers building AI-powered applications.

Qwen/Qwen2-0.5B-Instruct is a compact 0.5 billion parameter instruction-tuned language model from the Qwen2 series by Alibaba. It is optimized for conversational use cases and can be run locally via the Transformers library or through various inference providers. The model card provides usage examples for chat completion and supports multiple languages.