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.
* **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.
**ModelType:** Instruction-tuned large language model (7.61B parameters)
**License:**Apache 2.0
**Description:**
Qwen2.5-7B-Instruct is a powerful, instruction-following language model designed for advanced reasoning, coding, and multi-turn dialogue. Built on the Qwen2.5 architecture, it delivers state-of-the-art performance in understanding complex prompts, generating long-form text (up to 8K tokens), and handling structured outputs like JSON. It supports multilingual communication (29+ languages), including English, Chinese, and European languages, and excels in long-context tasks with support for up to 131,072 tokens.
Ideal for research, creative writing, coding assistance, and agent-based workflows, this model is optimized for real-world applications requiring robustness, accuracy, and scalability.
**KeyFeatures:**
- 7.61billion parameters
- Context length:131K tokens (supports long-context via YaRN)
- Strong performance in math, coding, and factual reasoning
- Fine-tuned for instruction following and chat interactions
- Deployable with Hugging Face Transformers, vLLM, and llama.cpp
**UseCase:**
Perfect for developers, researchers, and enterprises building intelligent assistants, autonomous agents, or content generation systems.
**DeepKAT-32B**is a high-performance, open-source coding agent built by merging two leading RL-tuned models—**DeepSWE-Preview** and **KAT-Dev**—on the **Qwen3-32B** base architecture using Arcee MergeKit’s TIES method. This 32B parameter model excels in complex software engineering tasks, including code generation, bug fixing, refactoring, and autonomous agent workflows with tool use.
Key strengths:
- Achieves ~62% SWE-Bench Verified score (on par with top open-source models).
- Strong performance in multi-file reasoning, multi-turn planning, and sparse reward environments.
- Optimized for agentic behavior with step-by-step reasoning and tool chaining.
Ideal for developers, AI researchers, and teams building intelligent code assistants or autonomous software agents.
Granite-4.0-1B is a lightweight, instruction-tuned language model designed for efficient on-device and research use. Built on a decoder-only dense transformer architecture, it delivers strong performance in instruction following, code generation, tool calling, and multilingual tasks—making it ideal for applications requiring low latency and minimal resource usage.
**KeyFeatures:**
- **Size:**1.6billion parameters (1B Dense), optimized for efficiency.
- **Capabilities:**
- Text generation, summarization, question answering
- Code completion and function calling (e.g., API integration)
*Awarm, enthusiastic, and empathetic reasoning model built on Qwen3-4B-Thinking*
**Overview**
Apollo-Astralis V1 4B is a 4-billion-parameter conversational AI designed for collaborative, emotionally intelligent problem-solving. Developed by VANTA Research, it combines rigorous logical reasoning with a vibrant, supportive communication style—making it ideal for creative brainstorming, educational support, and personal development.
**KeyFeatures**
- 🤔 **Explicit Reasoning**:Uses `</tool_call>` tags to break down thought processes step by step
- 💬 **Warm & Enthusiastic Tone**:Celebrates achievements with energy and empathy
- 🤝 **Collaborative Style**:Engages users with "we" language and clarifying questions
- 🔍 **High Accuracy**:Achieves 100% in enthusiasm detection and 90% in empathy recognition
- 🎯 **Fine-Tuned for Real-World Use**:Trained with LoRA on a dataset emphasizing emotional intelligence and consistency
**BaseModel**
Built on **Qwen3-4B-Thinking** and enhanced with lightweight LoRA fine-tuning (33M trainable parameters).
Available in both full and quantized (GGUF) formats via Hugging Face and Ollama.
**Type:**Large Language Model (LLM) – Text-Only (Vision-Language model stripped of vision components)
**Architecture:**Qwen3-VL, adapted for pure text generation
**Size:**32billion parameters
**License:**Apache 2.0
**Framework:**Hugging Face Transformers
---
### 🔍 **Description**
This is a **text-only variant** of the powerful **Qwen3-VL-32B-Instruct** multimodal model, stripped of its vision components to function as a high-performance pure language model. The model retains the full text understanding and generation capabilities of its parent — including strong reasoning, long-context handling (up to 32K+ tokens), and advanced multimodal training-derived coherence — while being optimized for text-only tasks.
It was created by loading the weights from the full Qwen3-VL-32B-Instruct model into a text-only Qwen3 architecture, preserving all linguistic and reasoning strengths without the need for image input.
Perfect for applications requiring deep reasoning, long-form content generation, code synthesis, and dialogue — with all the benefits of the Qwen3 series, now in a lightweight, text-focused form.
---
### 📌 Key Features
- ✅ **High-Performance Text Generation** – Built on top of the state-of-the-art Qwen3-VL architecture
- ✅ **Extended Context Length** – Supports up to 32,768 tokens (ideal for long documents and complex tasks)
- ✅ **Strong Reasoning & Planning** – Excels at logic, math, coding, and multi-step reasoning
- ✅ **Optimized for GGUF Format** – Available in multiple quantized versions (IQ3_M, Q2_K, etc.) for efficient inference on consumer hardware
- ✅ **Free to Use & Modify** – Apache 2.0 license
---
### 📦 Use Case Suggestions
- Long-form writing, summarization, and editing
- Code generation and debugging
- AI agents and task automation
- High-quality chat and dialogue systems
- Research and experimentation with large-scale LLMs on local devices
---
### 📚 References
- Original Model:[Qwen/Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct)
> ✅ **Note**:The model shown here is **not the original vision-language model** — it's a **text-only conversion** of the Qwen3-VL-32B-Instruct model, ideal for pure language tasks.
**BaseModel:** Qwen/Qwen3-VL-32B-Thinking (vanilla Qwen3-VL-32B with vision components removed)
**Architecture:**Transformer-based, 32-billion parameter model optimized for reasoning and complex text generation.
### Description:
Qwen3-VLTO-32B-Thinking is a pure text-only variant of the Qwen3-VL-32B-Thinking model, stripped of its vision capabilities while preserving the full reasoning and language understanding power. It is derived by transferring the weights from the vision-language model into a text-only transformer architecture, maintaining the same high-quality behavior for tasks such as logical reasoning, code generation, and dialogue.
This model is ideal for applications requiring deep linguistic reasoning and long-context understanding without image input. It supports advanced multimodal reasoning capabilities *in text form*—perfect for research, chatbots, and content generation.
### Key Features:
- ✅ 32B parameters, high reasoning capability
- ✅ No vision components — fully text-only
- ✅ Trained for complex thinking and step-by-step reasoning
- ✅ Compatible with Hugging Face Transformers and GGUF inference tools
- ✅ Available in multiple quantization levels (Q2_K to Q8_0) for efficient deployment
### Use Case:
Ideal for advanced text generation, logical inference, coding, and conversational AI where vision is not needed.
The **Gemma-3-The-Grand-Horror-27B-GGUF** model is a **fine-tuned version** of Google's **Gemma 3 27B** language model, specifically optimized for **extreme horror-themed text generation**. It was trained using the **Unsloth framework** on a custom in-house dataset of horror content, resulting in a model that produces vivid, graphic, and psychologically intense narratives—featuring gore, madness, and disturbing imagery—often even when prompts don't explicitly request horror.
Key characteristics:
- **BaseModel**:Gemma 3 27B (original by Google, not the quantized version)
- **Fine-tunedFor**:High-intensity horror storytelling, long-form narrative generation, and immersive scene creation
- **UseCase**:Creative writing, horror RP, dark fiction, and experimental storytelling
- **NotSuitable For**:General use, children, sensitive audiences, or content requiring neutral/positive tone
- **Quantization**:Available in GGUF format (e.g., q3k, q4, etc.), making it accessible for local inference on consumer hardware
> ✅ **Note**: The model card you see is for a **quantized, fine-tuned derivative**, not the original. The true base model is **Gemma 3 27B**, available at:https://huggingface.co/google/gemma-3-27b
This model is not for all audiences — it generates content with a consistently dark, unsettling tone. Use responsibly.
Qwen3-Nemotron-32B-RLBFF is a high-performance, fine-tuned large language model built on the Qwen3-32B foundation. It is specifically optimized to generate high-quality, helpful responses in a default thinking mode through advanced reinforcement learning with binary flexible feedback (RLBFF). Trained on the HelpSteer3 dataset, this model excels in reasoning, planning, coding, and information-seeking tasks while maintaining strong safety and alignment with human preferences.
**KeyPerformance (as of Sep 2025):**
- **MT-Bench:**9.50(near GPT-4-Turbo level)
- **ArenaHard V2:** 55.6%
- **WildBench:**70.33%
**Architecture&Efficiency:**
- 32billion parameters, based on the Qwen3 Transformer architecture
- Designed for deployment on NVIDIA GPUs (Ampere, Hopper, Turing)
- Achieves performance comparable to DeepSeek R1 and O3-mini at less than 5% of the inference cost
**UseCase:**
Ideal for applications requiring reliable, thoughtful, and safe responses—such as advanced chatbots, research assistants, and enterprise AI systems.
**Access&Usage:**
Available on Hugging Face with support for Hugging Face Transformers and vLLM.
**Cite:**[Wang et al., 2025 — RLBFF:Binary Flexible Feedback](https://arxiv.org/abs/2509.21319)
👉 *Note:The GGUF version (mradermacher/Qwen3-Nemotron-32B-RLBFF-i1-GGUF) is a user-quantized variant. The original model is available at nvidia/Qwen3-Nemotron-32B-RLBFF.*
**Note:**This model is a fine-tuned variant of the Qwen3 series, not a quantized version. The original base model is available at [qingy2024/Qwen3-VLTO-1.7B-Instruct](https://huggingface.co/qingy2024/Qwen3-VLTO-1.7B-Instruct) and was further adapted for horror-themed creative writing.
**IdealFor:** Creators, writers, and roleplayers seeking a compact, expressive model for immersive horror storytelling.
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