Qwen2.5 14B Instruct Alternatives

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.

Qwen2.5-72B-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.

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.

Qwen2.5-1.5B-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.

Qwen2.5-7B-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.

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.

A GGUF quantized version of Alibaba's Qwen2.5-Coder 14B Instruct model. It is optimized for code generation, completion, and reasoning tasks. The GGUF format allows efficient local execution using tools such as llama.cpp, LM Studio, and Ollama.

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 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-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 files for Qwen2.5-Coder-14B-Instruct, a specialized 14-billion parameter model for code generation and software development tasks. It supports advanced features such as tool calling and follows a chat template optimized for coding assistance. Ideal for local development environments and offline coding agents.

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-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.

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_Qwen3.6-27B-GGUF contains GGUF quantized files for the Qwen3.6-27B model. It supports text and vision inputs and is compatible with llama.cpp and other GGUF runtimes. Various quantization levels are provided to suit different hardware capabilities.

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 GGUF-quantized version of the Qwen3-4B-Instruct model optimized by Unsloth. It supports instruction following, tool calling, and efficient local inference. The model is distributed on Hugging Face for use with llama.cpp and similar runtimes.

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.

Qwen2.5-14B-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.

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.

This repository contains GGUF quantized versions of Alibaba's Qwen2.5-Coder-14B-Instruct model, optimized for use with LM Studio and other local LLM tools. It supports code generation, reasoning, and general instruction following with multiple quantization levels for different 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.

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.

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.

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-Coder-32B-Instruct-GGUF is a code-specialized instruction-tuned language model released in GGUF format on Hugging Face. It forms part of the Qwen2.5 series developed by Alibaba Cloud and is intended for local inference on compatible runtimes. The model includes a system prompt that identifies it as Qwen, created by Alibaba Cloud, and positions it as a helpful assistant. Its prompt template supports tool calling through a defined XML-based format that supplies function signatures inside tools tags and expects JSON-structured calls inside tool_call tags. This mechanism allows the model to invoke external functions when assisting with user queries. It is delivered as downloadable GGUF files hosted on the Hugging Face model repository. The GGUF format enables execution through local inference engines such as llama.cpp. No pricing information appears in the repository page, and the model is provided under the open terms typical of Hugging Face model uploads from the Qwen organization. The repository page supplies the chat template used by the model but does not list specific coding benchmarks, parameter counts, supported languages, or additional capabilities beyond the tool-calling structure shown in the prompt.

Qwen2.5-7B-Instruct-bnb-4bit is a quantized variant of Alibaba's Qwen2.5 7B Instruct model, provided by Unsloth. It uses bitsandbytes 4-bit quantization to enable efficient inference while maintaining strong performance on instruction following and tool use. The model is available on Hugging Face for developers building local or cloud LLM applications.

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 is a 4-bit quantized version of the Qwen2.5-3B-Instruct model created by Unsloth. It enables efficient local inference of a capable instruction-tuned LLM. The model is suitable for developers seeking to deploy language models with reduced memory requirements while maintaining strong performance.

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.