Qwen2.5 Math 7B Alternatives

Qwen2.5-Math-7B-Instruct is a foundation model hosted on Hugging Face that belongs to the Qwen2.5 series. It is designed to perform mathematical reasoning by following a default system instruction that directs it to reason step by step and enclose its final answer within a boxed format. The model includes a chat template that supplies this instruction when no system message is provided by the user. It also defines a tool-calling format that allows the model to invoke external functions during inference, with function signatures supplied inside XML-style tags and calls returned in a structured JSON object wrapped in custom XML tags. This template is expressed in a Jinja-style syntax that handles both system-prompt and tool scenarios. The model is delivered as a downloadable asset on the Hugging Face platform under the repository Qwen/Qwen2.5-Math-7B-Instruct. It can be loaded with the standard Transformers library for local or hosted inference. No pricing, licensing terms, or specific target audience beyond general model users are stated on the page.

Qwen2.5-Math-1.5B is a small but powerful model specifically trained for mathematical reasoning. It uses a specialized system prompt that encourages step-by-step thinking and final answer boxing. The model also supports tool calling for more complex mathematical tasks.

Qwen2.5-3B is part of Alibaba's Qwen2.5 family of open foundation models. The 3B variant offers a balance of performance and efficiency, supporting chat, reasoning, coding, and tool-calling use cases. It includes an advanced chat template with tool integration and is distributed as open weights on Hugging Face for flexible deployment.

Qwen2.5-Math-PRM-7B is a process reward model (PRM) designed to assess the correctness of individual reasoning steps in mathematical problem solving. Built on the Qwen2.5 architecture, it helps improve the reliability of math-focused LLMs by providing fine-grained feedback during generation. It is distributed openly on Hugging Face for research and integration into math reasoning pipelines.

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.

Qwen1.5-7B is the 7 billion parameter version of Alibaba's Qwen1.5 series of large language models. It supports the ChatML prompt format and is optimized for a wide range of natural language tasks. The model is available through the Hugging Face Transformers library and can be run locally or via Docker.

Qwen2-7B is the 7 billion parameter version of the Qwen2 series of large language models developed by Alibaba. It supports conversational chat, instruction following, and tool use. The model is distributed as open weights on Hugging Face and can be run locally with Transformers or inference engines. It is suitable for developers building AI applications that require strong language understanding and generation capabilities.

Qwen2-1.5B is part of the Qwen2 series of large language models developed by Alibaba. With 1.5 billion parameters, it offers a balance between performance and efficiency for text generation, instruction following, and conversational tasks. It supports a chat template for structured dialogue and is widely used as a base for further fine-tuning or direct inference.

Qwen2.5-0.5B is a 0.5 billion parameter model published on Hugging Face by the Qwen team. It belongs to the class of foundation models and implements a chat template that supports system messages, user and assistant roles, and a specific tool-calling format using XML-style tags for function signatures and JSON-structured calls. The provided chat template begins with a system prompt that defaults to "You are a helpful assistant." When tools are supplied it inserts a section describing available functions inside <tools> tags and instructs the model to respond with <tool_call> blocks containing a name and arguments object. The template handles both the newer Qwen2.5 chat format and a fallback legacy format, ensuring compatibility with conversational and tool-augmented interactions. The model is delivered as a repository on the Hugging Face platform, allowing download of weights and direct use through the Hugging Face ecosystem. No pricing, licensing terms, or additional deployment details appear in the repository metadata excerpt. The page title and surrounding interface elements indicate it forms part of the broader collection of openly accessible models hosted there.

Qwen2.5-14B is part of the Qwen2.5 series of large language models from Alibaba's Qwen team. It features strong performance on reasoning, coding, and multilingual tasks. The model includes a chat template supporting tool/function calling and is provided with full weights on Hugging Face for local inference or fine-tuning.

Qwen3.5-2B is a small yet powerful 2 billion parameter multimodal model from the Qwen series, optimized by Unsloth. It supports both text and vision inputs, making it suitable for on-device or resource-constrained applications. The model balances performance and efficiency for various language and vision-language tasks.

Qwen3.5-2B is an open-source large language model designed for text generation and understanding. It supports a wide range of NLP tasks and is suitable for developers and researchers seeking advanced language capabilities in their applications.

Qwen1.5-MoE-A2.7B is a Mixture-of-Experts (MoE) model from Alibaba's Qwen series. Despite having more total parameters, it activates only 2.7 billion parameters per token, offering a strong balance between performance and efficiency. It uses the ChatML format and is suitable for text generation, dialogue, and further fine-tuning.

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.

Qwen3.5-27B is a 27B parameter open-source large language model for advanced text generation and conversational AI. It is designed for AI researchers and developers building sophisticated NLP applications, with support for CLI, API, and Docker deployment.

Qwen3-4B-Thinking-2507 is a 4 billion parameter model optimized for deliberate, step-by-step reasoning and tool use. It features advanced prompting techniques for complex problem solving. It is designed for developers who need capable reasoning models that fit within limited compute budgets.

Qwen3.5-2B-Base is a compact open-weight language model developed by the Qwen team. It supports advanced features including tool calling, vision, and video understanding through specialized chat templates. The model is available on Hugging Face for local inference or fine-tuning using popular frameworks like Transformers.

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.

This is an AWQ-quantized 27B parameter version of Alibaba's Qwen3.5 language model. It supports text generation, tool use, and multimodal inputs while requiring significantly less memory than the original. The model is distributed via Hugging Face for use with popular inference frameworks.