Qwen2.5 VL 7B Instruct Alternatives

This GGUF-quantized version of Qwen3-VL-2B-Instruct enables efficient on-device or local-server multimodal inference. The model can process both text and images, supports tool calling, and is optimized for local use with tools such as llama.cpp. It is ideal for developers building vision-enhanced AI applications without relying on cloud APIs.

This is a GGUF-quantized version of Alibaba's Qwen2.5-VL-7B-Instruct model, optimized for local inference. It supports understanding both images and video content alongside text, making it suitable for multimodal applications. The model is popular in the LM Studio community for local AI use.

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.

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-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.

Qwen2.5-7B-Instruct-GGUF provides quantized GGUF files for the 7B parameter instruction-tuned version of Alibaba's Qwen2.5 model. It supports advanced features such as tool calling and is optimized for local inference using engines like llama.cpp. The model serves as a helpful assistant and can be integrated into applications via the Transformers library or GGUF-compatible runtimes.

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.

Qwen2.5-1.5B-Instruct-GGUF provides a quantized version of Alibaba's Qwen2.5 1.5B parameter instruction-tuned model in GGUF format. It supports local inference using tools like llama.cpp, Ollama, and LM Studio. The model excels at general chat, coding, and tool/function calling tasks while running efficiently on consumer hardware.

Qwen2.5-14B-Instruct-GGUF is a quantized variant of the Qwen2.5 14B Instruct model provided on Hugging Face. It supplies GGUF format files that enable local inference using compatible engines such as llama.cpp. The repository includes a specific chat template for the model. This template defines behavior for system prompts and supports tool calling through an XML-based format that supplies function signatures and expects JSON-structured calls wrapped in designated tags. When no system message is supplied the template defaults to identifying the model as Qwen created by Alibaba Cloud and positioning it as a helpful assistant. The files are hosted under the bartowski organization on the Hugging Face platform. This delivery method allows users to download the quantized weights directly and run them on consumer hardware without relying on remote API services. The presence of the GGUF extension indicates compatibility with the ecosystem of tools that consume this standardized format for on-device or self-hosted execution. No pricing information appears in the repository metadata. The model is distributed through the open platform that supports open-source and open-science initiatives.

Qwen2.5-72B-Instruct-GGUF is a quantized version of the Qwen2.5-72B-Instruct large language model provided in GGUF format on Hugging Face. It is distributed by bartowski and supports a specific chat template for instruction following and tool calling. The template defines behavior for system prompts, defaulting to the identity of Qwen created by Alibaba Cloud as a helpful assistant, and includes structured XML-based handling for function calls with JSON arguments when tools are supplied. The repository contains the necessary prompt formatting logic to enable the model to process messages, insert tool definitions within designated XML tags, and generate tool calls in a precise format enclosed in tool_call tags. This implementation allows the model to operate with one or more functions during inference. The GGUF format itself is intended to facilitate local execution through compatible engines. It belongs to the class of foundation models released for open use. No pricing, licensing terms, or specific hardware requirements are stated in the page content.

This is a GGUF quantized version of Alibaba's Qwen2.5-32B-Instruct model, optimized for local execution using tools like llama.cpp. It supports advanced features such as tool calling and follows specific chat templates. The model is designed for high-performance text generation and assistant-style interactions on consumer hardware.

A GGUF-quantized version of Alibaba's Qwen2.5-7B-Instruct model. It is optimized for local inference using tools such as llama.cpp or LM Studio. The model supports instruction following and general chat capabilities while running efficiently on consumer hardware.

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.

This Hugging Face repository hosts GGUF quantized versions of the Qwen2.5-7B-Instruct model created by Alibaba Cloud. The files are optimized for efficient local inference using tools such as llama.cpp, LM Studio, or Ollama. It includes chat templates and function calling support. The model is suitable for developers who want to run a strong open-source instruction-tuned LLM on consumer hardware without relying on cloud APIs.

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.

This is the GGUF quantized format of Alibaba's Qwen2.5-0.5B-Instruct model. It is optimized for local inference using tools like llama.cpp and supports tool calling and instruction following. The model is designed for efficient on-device or local CPU/GPU usage.

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.

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.

Qwen2.5-3B-Instruct-GGUF provides the 3 billion parameter version of Alibaba's Qwen2.5 instruction-tuned model in GGUF format for use with llama.cpp and compatible engines. It supports advanced features including tool calling and follows a detailed chat template. The model offers a strong balance between performance and efficiency for local deployment.

This is an Unsloth-optimized 4-bit quantized version of Alibaba's Qwen3-VL-2B-Instruct vision-language model. It supports understanding both images and text, making it suitable for multimodal tasks. The bnb-4bit format allows efficient local inference on modest hardware while preserving vision-language capabilities.

qwen2.5-1.5b-instruct.Q4_K_M.gguf is an open-source, quantized, instruction-tuned language model designed for efficient local inference. It enables developers and researchers to run advanced LLMs on their own hardware without relying on external APIs.

This is a GGUF-quantized release of Qwen3-235B-A22B, a massive mixture-of-experts language model optimized for local execution. It supports advanced reasoning, tool calling, and follows a specific chat template. Provided by Unsloth, it enables efficient inference of one of the largest open models using tools like llama.cpp.

Qwen2.5-VL-32B-Instruct-AWQ is a quantized version of Alibaba's large vision-language model. It accepts image, video, and text inputs and generates text outputs for tasks such as visual question answering, captioning, and document understanding. The AWQ quantization enables more efficient deployment while maintaining strong multimodal performance.

GGUF quantized versions of the Qwen3.5-2B model, optimized by Unsloth for fast local inference. Compatible with llama.cpp, Ollama, and other GGUF runtimes. Suitable for edge devices or low-memory environments while retaining strong language modeling performance.

Qwen3.5-122B-A10B-GGUF is a quantized release of the Qwen3.5-122B-A10B foundation model hosted on Hugging Face by Unsloth. It is provided in GGUF format for local inference. The model supports processing of text, image, and video inputs through a chat template that handles multimodal content. Its template includes specific tokens such as vision_start, vision_end, image_pad, and video_pad, along with logic for counting vision elements and raising exceptions for unsupported cases like videos in system messages. The template also accommodates tool calling by formatting available functions when tools are supplied in the messages. It is delivered as a repository on the Hugging Face platform containing GGUF quantized files. The page includes a chat template implementation in a macro-based format that processes message lists, handles different content types, and generates appropriate system prompts for tool use. The model belongs to the class of foundation models. No pricing, licensing details, or target audience beyond the repository context are stated.

Qwen2.5-VL-72B-Instruct is a large multimodal model capable of understanding both images and video alongside text. The AWQ quantized version allows efficient deployment. It supports advanced vision-language tasks and follows a chat-based instruction format, making it suitable for complex multimodal applications.

unsloth/Qwen3-VL-4B-Instruct-unsloth-bnb-4bit is a quantized 4B parameter vision-language model based on Qwen3-VL. Optimized by Unsloth for fast inference and fine-tuning, it supports image understanding, document parsing, and multimodal chat. The model uses BitsAndBytes 4-bit quantization.

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.

Qwen2.5-7B-Instruct is the 7B parameter instruction-tuned version of the Qwen2.5 model hosted on Hugging Face by Unsloth. It functions as a foundation model that follows a defined chat template for conversational and instruction-based interactions. The model incorporates explicit support for tool calling. Its prompt format instructs the model to use function signatures supplied inside XML-style tools tags and to emit calls inside tool_call tags containing JSON objects with function name and arguments. A default system prompt identifies the model as Qwen created by Alibaba Cloud and positions it as a helpful assistant. The template handles both cases with and without an initial system message from the user. It is delivered as downloadable model weights on the Hugging Face platform under the repository unsloth/Qwen2.5-7B-Instruct. The page belongs to the class of foundation models and is part of the broader open-source ecosystem promoted by Hugging Face for advancing artificial intelligence through open science. No pricing, licensing terms, or additional deployment formats are stated on the page.

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.

Qwen3-8B-GGUF is the GGUF-quantized format of Alibaba's Qwen3-8B model. This version enables efficient local inference on consumer hardware using tools like llama.cpp or Ollama. It retains strong language understanding and generation capabilities while supporting tool calling and structured prompting formats.

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 GGUF quantized version of the Qwen3-30B-A3B-Instruct model optimized for local use. It includes support for tool calling and follows an instruction-tuned format. The model is distributed by Unsloth and is compatible with popular GGUF inference engines.

Qwen3-14B-GGUF contains GGUF format files for the 14 billion parameter Qwen3 model, enabling efficient local execution using tools like llama.cpp. It supports advanced features such as tool calling and follows a specific chat template for multi-turn conversations. This allows developers to run a powerful open LLM on consumer hardware without relying on cloud APIs.

This is a 4-bit quantized (bitsandbytes) version of Alibaba's Qwen3-VL-32B-Instruct model, optimized by Unsloth for efficient inference. It supports vision-language tasks including image understanding, visual question answering, and document analysis. The model uses a chat template optimized for tool use and can be run locally or in notebooks.

A w8a8 quantized version of the Qwen2.5-VL-3B-Instruct model published by RedHatAI. It processes both text and visual inputs (images and video) and follows a multimodal chat template. Designed for developers building local multimodal applications with reduced memory requirements.

This is the GGUF quantized format of Alibaba's Qwen3-4B large language model. It enables efficient local inference using tools like llama.cpp or LM Studio. The model includes support for tool calling and follows a specific chat template for structured interactions.