Qwen2.5 Coder 32B Instruct Alternatives

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.

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.

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-AWQ is an open-source large language model hosted on Hugging Face. It belongs to the Qwen2.5-Coder series and is provided in an AWQ quantized format for efficient inference. The model includes a specific chat template that defines its instruction-following behavior. When a conversation begins without a system message it defaults to the prompt "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." The template also supports tool calling through a structured XML-based format that supplies function signatures and expects JSON arguments wrapped in tool_call tags. It is delivered as a downloadable model repository on the Hugging Face platform. The presence of the AWQ variant indicates it is intended for deployment scenarios that benefit from reduced memory usage and faster execution on compatible hardware. The page title and repository path confirm the exact identifier Qwen/Qwen2.5-Coder-32B-Instruct-AWQ. No pricing information is stated because the model is distributed through the open Hugging Face ecosystem. The surrounding site context emphasizes open source and open science, aligning with free access to the weights and associated template.

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.

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.

Qwen2.5-Coder-3B-Instruct is a 3 billion parameter instruction-tuned language model hosted on Hugging Face. It forms part of the Qwen2.5 series and is specialized for coding tasks. The model follows a system prompt that identifies it as Qwen, created by Alibaba Cloud, and positions it as a helpful assistant capable of using external tools when needed. The provided template defines a chat format that includes support for function calling. When tools are supplied, the model receives their signatures inside XML-style tags and is instructed to return calls in a structured JSON format wrapped in tool_call tags. This mechanism enables the model to invoke functions to assist with user queries. The template also handles both system and user messages with specific start and end tokens. It is delivered as an open model on the Hugging Face platform, where users can access the repository for download and inference. The page is part of Hugging Face's collection of models, datasets, and related resources aimed at advancing artificial intelligence through open source and open science.

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 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-Coder-7B-Instruct-GPTQ-Int4 is a quantized 7B parameter model hosted on Hugging Face. It belongs to the class of instruction-tuned large language models specialized for coding tasks. The model includes a specific chat template that defines its behavior for conversations. When the first message is a system prompt it uses that content; otherwise it defaults to the instruction that it is Qwen created by Alibaba Cloud and a helpful assistant. The template further supports tool calling by providing function signatures inside XML tags and instructing the model to return calls in a structured JSON format wrapped in tool_call tags. This enables the model to invoke external functions during interaction. It is delivered as a downloadable model repository on the Hugging Face platform. The GPTQ-Int4 designation indicates the model has been quantized to 4-bit integer precision using the GPTQ method, allowing it to run with reduced memory requirements compared to the full-precision version. The page provides the exact prompt format used by the model for consistent behavior across deployments. No pricing information appears because the artifact is freely downloadable.

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-Coder-7B-Instruct-AWQ is a 7 billion parameter model from Alibaba's Qwen2.5 series, specialized for coding tasks and instruction following. The AWQ-quantized version enables efficient deployment while retaining strong performance on code completion, debugging, and agentic programming workflows. It is distributed openly on Hugging Face.

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-Coder-14B-Instruct is a large language model hosted on Hugging Face for code-related tasks. It forms part of the Qwen2.5 series developed by Alibaba Cloud and follows a specific chat template that defines its behavior as a helpful assistant created by the company. The model implements a structured prompt format for handling conversations and tool use. When a system message is present it incorporates that content directly; otherwise it defaults to an internal system prompt identifying itself as Qwen from Alibaba Cloud. It supports function calling by accepting tool definitions inside XML-style tags and requires responses for tool invocations to appear inside designated XML tags containing JSON objects with function name and arguments. This format enables the model to process user queries that may involve external function calls. The model is delivered as an open model repository on the Hugging Face platform. Users can access it through the standard Hugging Face ecosystem for download, inference, or integration into applications. No pricing information is stated for the model itself.

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-Coder-7B-Instruct is an instruction-tuned language model hosted on Hugging Face. It forms part of the Qwen series developed by Alibaba Cloud and follows a default system prompt that identifies it as Qwen, a helpful assistant created by Alibaba Cloud. The model includes a chat template that supports system, user, and assistant messages. It also defines a specific format for tool use, allowing the model to call one or more functions when needed. Function signatures are supplied inside XML-style tools tags, and each call must be returned as a JSON object wrapped in tool_call tags. This structure enables the model to integrate external functions during interaction. The model is delivered as a downloadable asset on the Hugging Face platform, where it can be loaded for local or hosted inference. No pricing, licensing terms, or additional supported platforms appear in the provided page content. The entry is based solely on the model card and template details shown there.

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.

Qwen2.5-Coder-0.5B-Instruct is a compact 0.5 billion parameter model from the Qwen series, specialized for coding tasks. It supports code generation, completion, and understanding while being small enough to run efficiently on local devices. It is designed for developers seeking a lightweight coding assistant that can be self-hosted or integrated into IDEs.

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 massive Mixture-of-Experts coding model from the Qwen team, quantized to FP8. It excels at code generation, debugging, and following complex instructions. The model includes advanced tool-calling capabilities and is designed for software developers and AI coding agents.

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.

Qwen3-Coder-30B-A3B-Instruct-FP8 is an open-source large language model designed for code generation and instruction following. It enables developers to automate programming tasks and integrate advanced code understanding into their workflows. The model is available for local or cloud deployment and supports a variety of programming languages.

Qwen/Qwen3-Coder-30B-A3B-Instruct is an instruction-tuned large language model optimized for code generation and programming tasks. It is open source and suitable for developers and researchers seeking advanced AI coding assistants or tools.

Qwen2.5-32B-Instruct-GPTQ-Int8 is an INT8-quantized version of Alibaba's Qwen2.5 32B Instruct model. It supports advanced features including tool calling and follows a detailed chat template. The model is designed for efficient inference while retaining strong reasoning and instruction-following capabilities.

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.

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.

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.