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chore(model gallery): 🤖 add 1 new models via gallery agent (#6908)
chore(model gallery): 🤖 add new models via gallery agent Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
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@@ -22950,3 +22950,63 @@
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- filename: apollo-astralis-4b.i1-Q4_K_M.gguf
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sha256: 94e1d371420b03710fc7de030c1c06e75a356d9388210a134ee2adb4792a2626
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uri: huggingface://mradermacher/apollo-astralis-4b-i1-GGUF/apollo-astralis-4b.i1-Q4_K_M.gguf
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- !!merge <<: *qwen3
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name: "qwen3-vlto-32b-instruct-i1"
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urls:
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- https://huggingface.co/mradermacher/Qwen3-VLTO-32B-Instruct-i1-GGUF
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description: |
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**Model Name:** Qwen3-VL-32B-Instruct (Text-Only Variant: Qwen3-VLTO-32B-Instruct)
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**Base Model:** Qwen/Qwen3-VL-32B-Instruct
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**Repository:** [mradermacher/Qwen3-VLTO-32B-Instruct-i1-GGUF](https://huggingface.co/mradermacher/Qwen3-VLTO-32B-Instruct-i1-GGUF)
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**Type:** Large Language Model (LLM) – Text-Only (Vision-Language model stripped of vision components)
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**Architecture:** Qwen3-VL, adapted for pure text generation
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**Size:** 32 billion parameters
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**License:** Apache 2.0
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**Framework:** Hugging Face Transformers
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---
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### 🔍 **Description**
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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.
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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.
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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.
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---
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### 📌 Key Features
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- ✅ **High-Performance Text Generation** – Built on top of the state-of-the-art Qwen3-VL architecture
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- ✅ **Extended Context Length** – Supports up to 32,768 tokens (ideal for long documents and complex tasks)
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- ✅ **Strong Reasoning & Planning** – Excels at logic, math, coding, and multi-step reasoning
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- ✅ **Optimized for GGUF Format** – Available in multiple quantized versions (IQ3_M, Q2_K, etc.) for efficient inference on consumer hardware
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- ✅ **Free to Use & Modify** – Apache 2.0 license
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---
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### 📦 Use Case Suggestions
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- Long-form writing, summarization, and editing
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- Code generation and debugging
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- AI agents and task automation
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- High-quality chat and dialogue systems
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- Research and experimentation with large-scale LLMs on local devices
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---
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### 📚 References
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- Original Model: [Qwen/Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct)
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- Technical Report: [Qwen3 Technical Report (arXiv)](https://arxiv.org/abs/2505.09388)
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- Quantization by: [mradermacher](https://huggingface.co/mradermacher)
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> ✅ **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.
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overrides:
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parameters:
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model: Qwen3-VLTO-32B-Instruct.i1-Q4_K_S.gguf
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files:
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- filename: Qwen3-VLTO-32B-Instruct.i1-Q4_K_S.gguf
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sha256: 789d55249614cd1acee1a23278133cd56ca898472259fa2261f77d65ed7f8367
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uri: huggingface://mradermacher/Qwen3-VLTO-32B-Instruct-i1-GGUF/Qwen3-VLTO-32B-Instruct.i1-Q4_K_S.gguf
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