Qwen2.5 72B Instruct Alternatives

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.

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.

Qwen2.5-1.5B-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.

Qwen2.5-3B-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.

Qwen2.5-7B-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.

Qwen2.5-3B-Instruct-AWQ is a quantized version of Alibaba's Qwen2.5 3B instruction-tuned model. It supports tool calling and is optimized for efficient inference. The model is distributed on Hugging Face for use with local or cloud inference frameworks.

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.

Qwen2.5-32B-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-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.

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.

Qwen2.5-7B-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 7B Instruct is a 4-bit GPTQ quantized version of the 7B parameter instruction-tuned model from the Qwen2.5 series. It is hosted on Hugging Face under the repository name Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4. The model is presented as part of the Qwen family created by Alibaba Cloud. The provided page contains a chat template that defines its behavior for system prompts and tool use. When a conversation begins without an explicit system message it defaults to the statement that the model is Qwen created by Alibaba Cloud and is a helpful assistant. The template supports tool calling by supplying function signatures inside XML-style tools tags and instructs the model to respond with function calls wrapped in tool_call tags containing a JSON object with name and arguments fields. This structure enables the model to handle multiple tools in a single response. It is distributed as a model repository on the Hugging Face platform. The page title and repository identifier confirm the specific quantized variant.

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.

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.

Qwen2.5-72B-Instruct-FP8 is a quantized version of Alibaba's Qwen2.5 72B parameter model optimized for instruction following and function calling. It is hosted on Hugging Face and can be used with the Transformers library for text generation, chat, and tool-assisted tasks. The model is intended for developers building AI applications that require strong reasoning and multilingual capabilities.

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.

Qwen2.5-32B-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-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.

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.

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.

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.

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.