Qwen3.6 35B A3B Alternatives

Qwen3.6-35B-A3B-NVFP4 is a foundation model hosted on Hugging Face under the nvidia organization. The model processes multimodal inputs that include text, images, and video through a specialized chat template. Its template defines distinct handling for each content type, inserting vision-specific tokens such as vision_start, image_pad, video_pad, and vision_end while counting vision elements and raising exceptions for unsupported cases like videos in system messages. The template also supports tool calling by generating a system prompt that lists available functions when tools are supplied. It iterates over conversation messages, applies conditional formatting based on content type, and enforces rules such as requiring at least one message. The implementation appears as Jinja2-style macros that output structured strings compatible with the model's expected input format. This model is listed alongside standard Hugging Face infrastructure for models, datasets, spaces, and community resources. The associated organization page promotes open source and open science initiatives in artificial intelligence.

Qwen3.5-122B-A10B-NVFP4 is a quantized version of the Qwen3.5 multimodal model published by RedHatAI on Hugging Face. It supports vision and language capabilities with optimized performance for NVIDIA hardware using NVFP4 precision. The model is intended for developers integrating advanced multimodal AI into their applications or research.

This is a community-quantized version of the Qwen3.6-35B model using NVFP4 precision. It supports both text and vision inputs and is optimized for reduced memory usage and faster inference on compatible hardware. The model is intended for local deployment and experimentation.

Qwen3-32B-NVFP4 is an FP4 quantized version of the Qwen3-32B model created by NVIDIA. It includes support for tool calling and is optimized for high-performance inference on NVIDIA GPUs. The model is distributed on Hugging Face for developers seeking state-of-the-art performance with reduced memory and compute requirements.

Qwen3.6-27B-NVFP4 is an open-source large language model supporting both text and vision modalities. It can be deployed locally or in the cloud, making it suitable for AI researchers and developers who need flexible, high-capacity models for advanced NLP and computer vision tasks.

Qwen/Qwen3.6-35B-A3B-FP8 is an open-source large language model designed for advanced text generation and understanding. It supports instruction following and multilingual capabilities, making it suitable for developers and researchers building AI-powered solutions.

Qwen3.5-122B-A10B-NVFP4 is a highly quantized version of the large Qwen 3.5 model (122B parameters). It supports vision inputs alongside text and includes advanced tool-calling features. The model is distributed on Hugging Face for use in high-performance inference environments.

Qwen3.5-9B-NVFP4 is a quantized (NVFP4) version of Alibaba's Qwen3.5-9B large language model. It is optimized for reduced memory usage and faster inference while maintaining strong performance. The model supports multimodal inputs and can be run locally using standard Hugging Face tools.

Qwen3.5-35B-A3B is an open-source large language model supporting both text and multimodal inputs. It is designed for advanced AI applications, including chatbots and multimodal assistants, and is suitable for developers and researchers in AI.

Qwen3.6-27B-FP8 is a community-quantized variant of Alibaba's Qwen3 model offered in FP8 precision for lower memory usage while retaining strong performance. It supports text and vision inputs and is compatible with the Hugging Face ecosystem for local or cloud deployment. It is intended for developers building efficient multimodal applications.

Qwen3-30B-A3B-NVFP4 is an NVIDIA-optimized version of the Qwen3 model using NVFP4 quantization. It supports advanced tool calling and function calling through a detailed chat template. The model is designed for efficient inference on NVIDIA GPUs while preserving the capabilities of the original Qwen3 architecture.

Qwen3.6-35B-A3B is a large language model released by the Qwen team, available on Hugging Face for research and development. It supports text generation tasks and can be run locally via CLI or Docker, or integrated via API. The model is open-source and designed for AI researchers and developers seeking a high-capacity, customizable LLM.

This is a quantized (NVFP4) variant of the Qwen3.5-122B model optimized by NVIDIA for efficient inference. It supports advanced features including tool calling, multimodal inputs, and is designed to run on NVIDIA GPUs with significantly lower memory requirements than the original model. The model is distributed on Hugging Face and can be used with standard Transformers pipelines or custom inference servers.

This repository contains an NVIDIA-optimized FP4 (NVFP4) quantized version of the Qwen3 14B model. It includes specialized chat templates and tool-calling support optimized for NVIDIA inference stacks. The quantization enables faster and more memory-efficient inference while preserving model capabilities.

Qwen3.6-35B-A3B-MLX-VQ-3.4bpw is an open-source large language model available on Hugging Face, designed for natural language processing tasks. It supports local inference and can be integrated via API or CLI for research and development purposes. The model is suitable for AI researchers and developers seeking customizable LLMs.

Qwen3.6-35B-A3B is an open-source large language model checkpoint available on Hugging Face, designed for advanced natural language processing tasks. It enables AI researchers and developers to run, fine-tune, or integrate a state-of-the-art LLM in their own environments. The model supports local inference and experimentation for a wide range of text generation applications.

Qwen3.6-27B-NVFP4 is an FP4 quantized variant of the Qwen3.6-27B model. It supports both text and vision inputs including images and video through specialized tokens and templates. The model is designed for efficient local or hosted inference using the Transformers library and is available on the Hugging Face model hub.

Qwen3.6-35B-A3B-AWQ-4bit is a quantized version of a large language model, designed for efficient inference on local hardware. It is suitable for developers and researchers who need high-performance language models with reduced resource requirements.

Qwen3.6-27B-NVFP4 is an NVIDIA-optimized version of the Qwen3 27B model using NVFP4 quantization. It supports vision and video inputs in addition to text and includes a sophisticated tokenizer and chat template. The model is designed for efficient inference on NVIDIA GPUs while maintaining the strong performance of the original Qwen3 architecture.

A 4 billion parameter version of the Qwen3 large language model in FP8 precision. It supports instruction following, tool calling, and general text generation. The model is hosted on Hugging Face and can be used locally or via inference providers.

Qwen3.5-35B-A3B-Base is a dense Mixture-of-Experts language model from the Qwen series, offered as an open-weights model on Hugging Face. It supports text generation, multimodal inputs including vision and video, and advanced features such as tool calling. Developers can run it locally via pip or Docker, or use it through cloud inference providers.

Qwen3.5-4B is an open-source large language model designed for text generation and conversational AI. It is suitable for developers and researchers building advanced natural language processing applications and supports integration via API and CLI.

Qwen3.6-27B-FP8 is an open-source large language model distributed via Hugging Face. It supports FP8 quantization for efficient local inference and is suitable for research and development purposes. The model is accessible to AI researchers and developers.

This NVIDIA-hosted quantized version of the Qwen 3.5 397B (with 17B active parameters) model supports vision and video inputs in addition to text. It includes advanced prompting templates for tool use and multimodal content. The model is designed for high-performance inference using NVIDIA-optimized stacks.