Qwen2.5 7B Instruct Alternatives

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.

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.

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.

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.

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.

Qwen2.5-3B-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.

Qwen3-4B-Instruct-2507-GGUF is a GGUF-formatted, quantized version of Alibaba's Qwen3 4B instruct model. It supports local execution with tools such as llama.cpp and is optimized for efficient inference on consumer hardware. The model includes advanced tool-calling capabilities and is suitable for developers building local AI applications.

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.

Qwen2.5-VL-7B-Instruct-GGUF is a repository on Hugging Face that supplies GGUF quantized files of the Qwen2.5-VL 7B vision-language model. It is intended for local inference of a multimodal model capable of processing both images and video alongside text. The repository is provided by unsloth. The files include variants such as Qwen2.5-VL-7B-Instruct-BF16.gguf and Qwen2.5-VL-7B-Instruct-IQ4_NL.gguf. A chat template is defined that handles messages containing text, image, or video content by inserting specific vision and image or video pad tokens. The template supports counting multiple images or videos in a conversation and adds optional identifiers such as "Picture 1:" or "Video 1:" before the visual tokens. It uses special tokens including vision_start, vision_end, image_pad, video_pad, im_start, and im_end. The total file size across the GGUF files is 15237851776 bytes. These quantized models are distributed for use with compatible inference engines that accept the GGUF format. The repository forms part of the broader collection of foundation models available on the platform. No pricing, licensing terms, or additional deployment details beyond the GGUF files and chat template are stated.

This repository hosts GGUF quantized weights of Alibaba's Qwen3 8B model, optimized for use with llama.cpp, LM Studio, and other local LLM runners. It supports tool calling and follows the latest Qwen3 chat template. The model is intended for developers and enthusiasts who want to run a capable 8B-parameter LLM locally without relying on cloud APIs.

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 repository contains GGUF quantized files for the Qwen3-4B model, enabling efficient local inference with tools such as llama.cpp. It includes support for tool calling and follows the Qwen chat template. The models are intended for developers seeking lightweight, locally runnable large language models.

Qwen3-1.7B-GGUF contains GGUF quantized weights for the 1.7 billion parameter version of the Qwen3 large language model. Created by MaziyarPanahi, these files enable efficient local inference using tools like llama.cpp or Ollama. The model supports advanced features including tool calling and is suitable for on-device or private deployment scenarios.

Qwen3-0.6B-GGUF is a quantized version of Alibaba's Qwen3 0.6 billion parameter language model provided in GGUF format for use with llama.cpp and other local inference tools. It includes a chat template supporting tool calling and is suitable for resource-constrained environments. The model is hosted on Hugging Face and can be used for text generation and conversational tasks.

GGUF quantized conversion of Alibaba's Qwen3-14B model, optimized for local inference with tools such as llama.cpp. It includes support for tool calling, system prompts, and multi-turn conversations. Maintained by the community quantizer MaziyarPanahi for easy local deployment.

Qwen2.5-32B-Instruct-GPTQ-Int4 is a quantized variant of the Qwen2.5 32B instruction-tuned language model hosted on Hugging Face. It is provided as a GPTQ-Int4 model file intended for inference on compatible hardware. The model follows a system prompt that identifies it as Qwen created by Alibaba Cloud and positions it as a helpful assistant. The page supplies a chat template that defines how the model processes messages. When a system message is present it uses that content; otherwise it defaults to stating that the model is Qwen created by Alibaba Cloud and is a helpful assistant. The template also includes explicit support for tool use. It instructs the model that it may call one or more functions to assist with a user query, supplies function signatures inside XML-style tools tags, and requires each function call to be returned as a JSON object wrapped in tool_call XML tags. This structure enables the model to handle tool calling and function calling formats during generation. The model is distributed through the Hugging Face repository at Qwen/Qwen2.5-32B-Instruct-GPTQ-Int4. It belongs to the class of foundation models made available for download and local or hosted inference.

Qwen3-32B-GGUF contains GGUF quantized weights for the 32 billion parameter version of Alibaba's Qwen3 large language model. These files allow efficient local execution on consumer or server hardware using GGUF-compatible runtimes. The model excels at complex reasoning, coding, and multilingual tasks.

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.

Qwen2.5-Coder-7B-Instruct-GGUF contains quantized GGUF files for Alibaba's Qwen2.5-Coder 7B Instruct model. Optimized for code generation and software development tasks, it includes advanced tool calling and function calling capabilities. The model can be run locally with llama.cpp or other GGUF-compatible engines.

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.

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.

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.

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.

Qwen2.5-32B-Instruct is an open-source large language model designed for instruction-following and conversational AI tasks. Developed by Alibaba Cloud, it supports text generation, multi-turn dialogue, and custom fine-tuning. It is suitable for AI researchers and developers seeking a powerful, adaptable LLM for various natural language processing applications.

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.

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.

Qwen2.5-7B-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.

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.

Qwen2.5-1.5B-Instruct is a 1.5 billion parameter instruction-tuned language model hosted on Hugging Face. It forms part of the Qwen2.5 series and is made available as an open model for text-based tasks. The model includes a specific chat template that defines how it processes conversation history. When the first message is a system prompt it incorporates that content directly; otherwise it defaults to the instruction "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." The template further supports tool use by inserting function signatures inside XML-style <tools> tags and instructing the model to emit calls inside <tool_call> tags containing JSON objects with name and arguments fields. This structure enables the model to handle multi-turn dialogues that may involve external function invocation. It is delivered as a downloadable model repository on the Hugging Face platform, allowing integration into applications that support the Transformers library or compatible inference runtimes. The page presents the model under the organization's open-source efforts, consistent with Hugging Face's focus on open models and open science.

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-Coder-1.5B-Instruct-GGUF contains quantized GGUF files for the 1.5 billion parameter instruction-tuned coding model from the Qwen2.5 family. Optimized for local execution, it supports code generation, debugging, and tool calling while maintaining strong performance for its size. Suitable for developers needing on-device or offline coding assistance.