Qwen2.5 7B Instruct Unsloth Bnb Alternatives

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.

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.

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.

This is an Unsloth-optimized 4-bit quantized version of Alibaba's Qwen3-VL-2B-Instruct vision-language model. It supports understanding both images and text, making it suitable for multimodal tasks. The bnb-4bit format allows efficient local inference on modest hardware while preserving vision-language capabilities.

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.

This is a bitsandbytes 4-bit quantized version of the Qwen2.5-14B model prepared by Unsloth. It is designed for rapid fine-tuning and inference while maintaining strong multilingual performance across Chinese, English, French, Spanish, Portuguese, German and other languages. The model is compatible with the Hugging Face ecosystem and Unsloth's accelerated training tools.

This is a quantized 4-bit version of the Qwen3-8B large language model prepared by Unsloth. It includes optimized inference code, a chat template with tool-calling support, and is designed for local execution with significantly lower memory requirements than the original model. It is distributed on Hugging Face for developers building LLM applications.

unsloth/Qwen3-VL-4B-Instruct-unsloth-bnb-4bit is a quantized 4B parameter vision-language model based on Qwen3-VL. Optimized by Unsloth for fast inference and fine-tuning, it supports image understanding, document parsing, and multimodal chat. The model uses BitsAndBytes 4-bit quantization.

unsloth/Qwen3-14B-unsloth-bnb-4bit is a 4-bit quantized version of Qwen3-14B created with Unsloth optimizations. It supports dramatically faster fine-tuning and inference while maintaining high accuracy. The model includes advanced function calling and tool-use capabilities via its chat template.

A quantized 4B parameter version of the Qwen3 model prepared for use with Unsloth. It supports efficient inference and fine-tuning on consumer hardware using bitsandbytes 4-bit quantization. The model includes optimized chat templates and tool-calling capabilities.

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.

Qwen3-32B-bnb-4bit is a bitsandbytes 4-bit quantized version of the Qwen3-32B model, optimized for use with the Unsloth library. It supports advanced features such as tool calling and is designed for efficient fine-tuning and inference on GPUs with limited VRAM.

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.

This is a 4-bit quantized (bitsandbytes) version of Alibaba's Qwen3-VL-32B-Instruct model, optimized by Unsloth for efficient inference. It supports vision-language tasks including image understanding, visual question answering, and document analysis. The model uses a chat template optimized for tool use and can be run locally or in notebooks.

This is a GGUF quantized version of the Qwen3-30B-A3B-Instruct model optimized for local use. It includes support for tool calling and follows an instruction-tuned format. The model is distributed by Unsloth and is compatible with popular GGUF inference engines.

Qwen2.5-Coder-7B-Instruct-bnb-4bit is a quantized language model hosted on Hugging Face. It is the 4-bit version of the Qwen2.5-Coder-7B-Instruct model prepared by Unsloth and distributed for inference use. The model follows a specific chat template that begins with a system prompt identifying it as Qwen created by Alibaba Cloud and designates it as a helpful assistant. The template includes support for tool calling through an XML-based format that supplies function signatures inside tools tags and expects responses inside tool_call tags containing JSON with name and arguments fields. This structure enables the model to process messages and invoke external functions when required. It is delivered as a repository on the Hugging Face platform under the identifier unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit. Users obtain the model files through the standard Hugging Face ecosystem for loading with compatible inference libraries. The page belongs to the collection of open models that advance artificial intelligence through open source and open science. No pricing, licensing terms, or additional capabilities beyond the listed prompt format appear in the repository metadata.

This is a GGUF-quantized version of the Qwen3-4B-Instruct model optimized by Unsloth. It supports instruction following, tool calling, and efficient local inference. The model is distributed on Hugging Face for use with llama.cpp and similar runtimes.

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.

This is a 4-bit quantized version of the Qwen3-VL-4B vision-language model, optimized by Unsloth using bitsandbytes. It allows efficient multimodal inference combining text and image inputs on resource-constrained devices. The model includes a specialized chat template for vision tasks and is distributed via Hugging Face for local use with pip installable libraries.

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.

Qwen3.5-4B is a compact multimodal model from the Qwen series, optimized by Unsloth for faster inference. It supports both text and vision inputs with a custom chat template. The model is distributed on Hugging Face and can be used with standard transformers or Unsloth libraries.

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.

Qwen2.5-14B-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.

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.

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.

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.

Qwen3-4B-GGUF provides a quantized version of the Qwen3 4B parameter model in GGUF format, optimized for efficient inference and fine-tuning using Unsloth. It supports local execution on consumer hardware with features like chat templates and tool calling capabilities. Primarily used by developers and researchers looking to run or customize open-weight language models without relying on cloud APIs.

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.

This repository provides GGUF quantized weights for the 0.6 billion parameter Qwen3 model. It is optimized for use with Unsloth, supporting fast fine-tuning and inference. The model includes advanced features such as tool calling and is designed for users who want a lightweight yet powerful open LLM that runs locally.

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.