Qwen2.5 7B Instruct Alternatives

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.

Qwen2.5-14B-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.

Qwen2.5-1.5B-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.

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.

Qwen2-1.5B-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.

Qwen2.5-14B-Instruct-AWQ is an open-source large language model designed for instruction following and conversational tasks. It provides downloadable weights and supports local inference, making it suitable for researchers and developers seeking customizable LLM solutions.

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.

Qwen2.5-7B-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.

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.

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-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-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.

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.

Qwen3-4B-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.

Qwen2.5-32B-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.

Qwen2.5-7B-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.

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-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.

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.

Part of the Qwen2.5 series, this 1.5 billion parameter model is fine-tuned specifically for mathematical tasks. It excels at step-by-step reasoning, equation solving, and can utilize tools when needed. The model uses a specialized chat template that encourages boxed final answers and is available for download on Hugging Face.

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.

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-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.