Qwen2.5 7B Instruct Alternatives

Unsloth's optimized version of the Qwen2.5-14B-Instruct model. It supports efficient fine-tuning and inference with lower memory usage. The model includes a chat template for instruction following and tool calling capabilities. It is designed for developers who want to run or fine-tune large language models locally or in the cloud.

Qwen2.5 7B Instruct Unsloth Bnb is a 4-bit quantized version of the Qwen2.5-7B-Instruct language model hosted on Hugging Face. It is prepared using Unsloth and BitsAndBytes quantization for reduced memory usage during inference and fine-tuning. The model includes a specific chat template that begins with a default system prompt identifying it as Qwen created by Alibaba Cloud and designates it as a helpful assistant. The template supports tool calling by providing function signatures inside XML-style tags and instructs the model to return calls in a structured JSON format wrapped in tool_call tags. This enables the model to handle multi-turn conversations that incorporate external function invocations when required. It is delivered as a model repository on the Hugging Face platform under the identifier unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit. The repository contains the necessary files to load the quantized weights directly into compatible inference or training frameworks that support 4-bit bnb quantization. The model belongs to the class of foundation models. No pricing, licensing terms, or additional capabilities 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.

Qwen2.5-0.5B-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.

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.

Qwen2.5-14B-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.

Qwen2.5-7B-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.

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.

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.

Qwen2-1.5B-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.

This is a 4-bit quantized version of the Qwen2.5-3B-Instruct model prepared with Unsloth and bitsandbytes. It enables developers to fine-tune LLMs significantly faster and with much lower memory usage compared to standard methods. The model is hosted on Hugging Face and is intended for efficient continued pre-training or instruction tuning.

This is a 4-bit quantized version of the Qwen3-4B-Instruct model created with Unsloth and bitsandbytes. It supports efficient inference and includes a chat template for tool calling and instruction following. The model is hosted on Hugging Face and can be used with the Transformers library.

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.

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.

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.

Qwen2.5-32B-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.

Qwen2.5-1.5B-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-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.

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.

qwen2.5-1.5b-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.