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qwen3-coder-next-mxfp4_moe
Repository: localai
qwen3-coder-next-mxfp4_moe
The model is a quantized version of **Qwen/Qwen3-Coder-Next** (base model) using the **MXFP4** quantization scheme. It is optimized for efficiency while retaining performance, suitable for deployment in applications requiring lightweight inference. The quantized version is tailored for specific tasks, with parameters like temperature=1.0 and top_p=0.95 recommended for generation.
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huihui-qwen3-vl-30b-a3b-instruct-abliterated-mxfp4_moe
Repository: localaiLicense: apache-2.0
huihui-qwen3-vl-30b-a3b-instruct-abliterated-mxfp4_moe
**Model Name:** Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated **Base Model:** Qwen3-VL-30B (a large multimodal language model) **Repository:** [huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated) **Quantization:** MXFP4_MOE (GGUF format, optimized for inference on consumer hardware) **Model Type:** Instruction-tuned, multimodal (text + vision) **Size:** 30 billion parameters (MoE architecture with active 3.7B parameters per token) **License:** Apache 2.0 **Description:** Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated is an advanced, instruction-tuned multimodal large language model based on Qwen3-VL-30B, enhanced with a mixture-of-experts (MoE) architecture and fine-tuned for strong reasoning, visual understanding, and dialogue capabilities. It supports both text and image inputs, making it suitable for tasks such as image captioning, visual question answering, and complex instruction following. This version is quantized using MXFP4_MOE for efficient inference while preserving high performance. Ideal for developers and researchers seeking a powerful, efficient, and open-source multimodal model for real-world applications. > 🔍 *Note: This is a text-only version.*
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