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qwen3-vl-30b-a3b-instruct
Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.
Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on-demand deployment.
#### Key Enhancements:
* **Visual Agent**: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks.
* **Visual Coding Boost**: Generates Draw.io/HTML/CSS/JS from images/videos.
* **Advanced Spatial Perception**: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.
* **Long Context & Video Understanding**: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.
* **Enhanced Multimodal Reasoning**: Excels in STEM/Math—causal analysis and logical, evidence-based answers.
* **Upgraded Visual Recognition**: Broader, higher-quality pretraining is able to “recognize everything”—celebrities, anime, products, landmarks, flora/fauna, etc.
* **Expanded OCR**: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.
* **Text Understanding on par with pure LLMs**: Seamless text–vision fusion for lossless, unified comprehension.
#### Model Architecture Updates:
1. **Interleaved-MRoPE**: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.
2. **DeepStack**: Fuses multi‑level ViT features to capture fine-grained details and sharpen image–text alignment.
3. **Text–Timestamp Alignment:** Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling.
This is the weight repository for Qwen3-VL-30B-A3B-Instruct.
Repository: localaiLicense: apache-2.0
huihui-qwen3-vl-30b-a3b-instruct-abliterated
These are quantizations of the model Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated-GGUF
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.*
Repository: localaiLicense: apache-2.0