Qwen2.5 72B Instruct FP8 Dynamic Alternatives

This is a quantized version of the Qwen2.5-VL-3B vision-language model optimized with FP8 dynamic quantization. It can understand and reason about images, videos, and text together. The model is provided by RedHatAI and is suitable for multimodal AI applications.

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.

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.

Qwen2.5-32B-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.

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.

A w8a8 quantized version of the Qwen2.5-VL-3B-Instruct model published by RedHatAI. It processes both text and visual inputs (images and video) and follows a multimodal chat template. Designed for developers building local multimodal applications with reduced memory requirements.

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.

Qwen2.5-14B-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.

Qwen3-235B-A22B is a 235 billion parameter Mixture-of-Experts model with 22 billion active parameters, provided in FP8 precision. It is an instruction-tuned model supporting advanced features such as tool calling. The model represents the latest generation of the Qwen series and is available for local and hosted inference.

Qwen3-30B-A3B-Instruct-2507-FP8 is an FP8 quantized variant of Alibaba's Qwen3 30B-A3B Mixture-of-Experts instruct model. It supports advanced features such as tool calling and is optimized for efficient inference. The model is hosted on Hugging Face and is suitable for developers needing high-performance language capabilities with lower memory footprint.

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.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.

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.

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.

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.

Qwen2.5-7B-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.

Qwen2.5-1.5B-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.

Qwen2.5-7B-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-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.

Qwen3-VL-235B-A22B-Instruct-FP8 is a massive multimodal model from the Qwen3 family, combining 235B and 22B parameters in a Mixture-of-Experts architecture. It is instruction-tuned for vision-language tasks and provided in an FP8 quantized format for more efficient inference. The model supports advanced tool use and multimodal understanding.

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.

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.

Qwen2.5-72B-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.

Qwen2.5-3B-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.

Qwen3-Coder-Next-FP8-dynamic is a dynamically quantized FP8 version of the Qwen3 coding model, optimized for efficient inference. It supports advanced code generation and instruction following capabilities while reducing memory and compute requirements. The model is hosted on Hugging Face and is designed for developers seeking high-performance local coding assistants.