Qwen2.5 Coder 7B Instruct Bnb Alternatives

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-Coder-7B-Instruct-GGUF contains quantized GGUF files for Alibaba's Qwen2.5-Coder 7B Instruct model. Optimized for code generation and software development tasks, it includes advanced tool calling and function calling capabilities. The model can be run locally with llama.cpp or other GGUF-compatible engines.

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-Coder-7B-Instruct-AWQ is a 7 billion parameter model from Alibaba's Qwen2.5 series, specialized for coding tasks and instruction following. The AWQ-quantized version enables efficient deployment while retaining strong performance on code completion, debugging, and agentic programming workflows. It is distributed openly on Hugging Face.

Qwen2.5-Coder-7B-Instruct-GPTQ-Int4 is a quantized 7B parameter model hosted on Hugging Face. It belongs to the class of instruction-tuned large language models specialized for coding tasks. The model includes a specific chat template that defines its behavior for conversations. When the first message is a system prompt it uses that content; otherwise it defaults to the instruction that it is Qwen created by Alibaba Cloud and a helpful assistant. The template further supports tool calling by providing function signatures inside XML tags and instructing the model to return calls in a structured JSON format wrapped in tool_call tags. This enables the model to invoke external functions during interaction. It is delivered as a downloadable model repository on the Hugging Face platform. The GPTQ-Int4 designation indicates the model has been quantized to 4-bit integer precision using the GPTQ method, allowing it to run with reduced memory requirements compared to the full-precision version. The page provides the exact prompt format used by the model for consistent behavior across deployments. No pricing information appears because the artifact is freely downloadable.

Qwen2.5-Coder-7B-Instruct-AWQ is a quantized version of Alibaba's Qwen2.5 Coder model optimized for code generation and software development tasks. It supports instruction following and tool use. The model is intended for developers seeking a capable coding assistant that can run locally or in private environments.

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-Coder-7B-Instruct is an instruction-tuned language model hosted on Hugging Face. It forms part of the Qwen series developed by Alibaba Cloud and follows a default system prompt that identifies it as Qwen, a helpful assistant created by Alibaba Cloud. The model includes a chat template that supports system, user, and assistant messages. It also defines a specific format for tool use, allowing the model to call one or more functions when needed. Function signatures are supplied inside XML-style tools tags, and each call must be returned as a JSON object wrapped in tool_call tags. This structure enables the model to integrate external functions during interaction. The model is delivered as a downloadable asset on the Hugging Face platform, where it can be loaded for local or hosted inference. No pricing, licensing terms, or additional supported platforms appear in the provided page content. The entry is based solely on the model card and template details shown there.

A 4-bit quantized version of Qwen2.5-Coder-14B-Instruct optimized for the MLX framework on Apple Silicon. It supports code generation, instruction following, and tool calling. The model is distributed in a format suitable for local execution via MLX or compatible runners like LM Studio.

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 repository hosts GGUF quantized files for Qwen2.5-Coder-14B-Instruct, a specialized 14-billion parameter model for code generation and software development tasks. It supports advanced features such as tool calling and follows a chat template optimized for coding assistance. Ideal for local development environments and offline coding agents.

A GGUF quantized version of Alibaba's Qwen2.5-Coder 14B Instruct model. It is optimized for code generation, completion, and reasoning tasks. The GGUF format allows efficient local execution using tools such as llama.cpp, LM Studio, and Ollama.

Qwen2.5-Coder-3B-Instruct is a 3 billion parameter instruction-tuned language model hosted on Hugging Face. It forms part of the Qwen2.5 series and is specialized for coding tasks. The model follows a system prompt that identifies it as Qwen, created by Alibaba Cloud, and positions it as a helpful assistant capable of using external tools when needed. The provided template defines a chat format that includes support for function calling. When tools are supplied, the model receives their signatures inside XML-style tags and is instructed to return calls in a structured JSON format wrapped in tool_call tags. This mechanism enables the model to invoke functions to assist with user queries. The template also handles both system and user messages with specific start and end tokens. It is delivered as an open model on the Hugging Face platform, where users can access the repository for download and inference. The page is part of Hugging Face's collection of models, datasets, and related resources aimed at advancing artificial intelligence through open source and open science.

Qwen2.5-Coder-1.5B-Instruct-GGUF contains quantized GGUF files for the 1.5 billion parameter instruction-tuned coding model from the Qwen2.5 family. Optimized for local execution, it supports code generation, debugging, and tool calling while maintaining strong performance for its size. Suitable for developers needing on-device or offline coding assistance.

Qwen2.5-Coder-14B-Instruct is a large language model hosted on Hugging Face for code-related tasks. It forms part of the Qwen2.5 series developed by Alibaba Cloud and follows a specific chat template that defines its behavior as a helpful assistant created by the company. The model implements a structured prompt format for handling conversations and tool use. When a system message is present it incorporates that content directly; otherwise it defaults to an internal system prompt identifying itself as Qwen from Alibaba Cloud. It supports function calling by accepting tool definitions inside XML-style tags and requires responses for tool invocations to appear inside designated XML tags containing JSON objects with function name and arguments. This format enables the model to process user queries that may involve external function calls. The model is delivered as an open model repository on the Hugging Face platform. Users can access it through the standard Hugging Face ecosystem for download, inference, or integration into applications. No pricing information is stated for the model itself.

A quantized variant of the Qwen3-Coder model using AWQ 4-bit compression. It is designed for local inference of code generation and programming assistance tasks while significantly lowering VRAM usage compared to the original model. The model includes a chat template suitable for instruction following and tool use.

This is an 8-bit quantized version of the Qwen2.5-Coder-14B-Instruct model, optimized for the MLX framework used on Apple Silicon devices. It includes a specialized chat template for instruction following and tool use. The model is suitable for local code generation and programming assistance tasks.

Qwen2.5-Coder-32B-Instruct-AWQ is an open-source large language model hosted on Hugging Face. It belongs to the Qwen2.5-Coder series and is provided in an AWQ quantized format for efficient inference. The model includes a specific chat template that defines its instruction-following behavior. When a conversation begins without a system message it defaults to the prompt "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." The template also supports tool calling through a structured XML-based format that supplies function signatures and expects JSON arguments wrapped in tool_call tags. It is delivered as a downloadable model repository on the Hugging Face platform. The presence of the AWQ variant indicates it is intended for deployment scenarios that benefit from reduced memory usage and faster execution on compatible hardware. The page title and repository path confirm the exact identifier Qwen/Qwen2.5-Coder-32B-Instruct-AWQ. No pricing information is stated because the model is distributed through the open Hugging Face ecosystem. The surrounding site context emphasizes open source and open science, aligning with free access to the weights and associated template.

Qwen2.5-Coder-0.5B-Instruct is a compact 0.5 billion parameter model from the Qwen series, specialized for coding tasks. It supports code generation, completion, and understanding while being small enough to run efficiently on local devices. It is designed for developers seeking a lightweight coding assistant that can be self-hosted or integrated into IDEs.

This repository contains GGUF quantized versions of Alibaba's Qwen2.5-Coder-14B-Instruct model, optimized for use with LM Studio and other local LLM tools. It supports code generation, reasoning, and general instruction following with multiple quantization levels for different hardware.

A massive Mixture-of-Experts coding model from the Qwen team, quantized to FP8. It excels at code generation, debugging, and following complex instructions. The model includes advanced tool-calling capabilities and is designed for software developers and AI coding agents.

stelterlab/Qwen3-Coder-30B-A3B-Instruct-AWQ is a quantized version of a large language model hosted on Hugging Face. It belongs to the class of coding-focused AI models and provides an AWQ-quantized instruct variant intended for local or self-hosted inference. The repository contains model weights, tokenizer configuration, and a chat template in Jinja format. The tokenizer uses specific pad, bos, and eos tokens along with an end-of-text token. The provided chat template supports structured rendering of item lists, conditional system messages, and tool definitions for multi-turn conversations. It is delivered as downloadable files on the Hugging Face platform. Users obtain the model through the standard repository interface for use with compatible inference frameworks that support AWQ quantization and the included configuration files. No pricing, licensing terms, or named maker beyond the repository owner are stated in the page content. The surrounding Hugging Face site promotes open source and open science but does not attach specific license details to this model card.

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 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 MLX version of the Qwen3-Coder 30B (A3B Instruct) model. It is a coding-specialized large language model optimized for local execution on Apple hardware. The model excels at code generation, understanding, and related programming tasks while maintaining a manageable memory footprint.

Qwen2.5-Coder-32B-Instruct-GGUF is a code-specialized instruction-tuned language model released in GGUF format on Hugging Face. It forms part of the Qwen2.5 series developed by Alibaba Cloud and is intended for local inference on compatible runtimes. The model includes a system prompt that identifies it as Qwen, created by Alibaba Cloud, and positions it as a helpful assistant. Its prompt template supports tool calling through a defined XML-based format that supplies function signatures inside tools tags and expects JSON-structured calls inside tool_call tags. This mechanism allows the model to invoke external functions when assisting with user queries. It is delivered as downloadable GGUF files hosted on the Hugging Face model repository. The GGUF format enables execution through local inference engines such as llama.cpp. No pricing information appears in the repository page, and the model is provided under the open terms typical of Hugging Face model uploads from the Qwen organization. The repository page supplies the chat template used by the model but does not list specific coding benchmarks, parameter counts, supported languages, or additional capabilities beyond the tool-calling structure shown in the prompt.