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Qwen3-VL-8B-Instruct-Unredacted-MAX.mmproj-q8_0.gguf
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---
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license: apache-2.0
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tags:
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- text-generation-inference
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- uncensored
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- abliterated
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- unfiltered
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- unredacted
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- max
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- llama.cpp
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- legal
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base_model:
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- prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX
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language:
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- en
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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# **Qwen3-VL-8B-Instruct-Unredacted-MAX-GGUF**
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> Qwen3-VL-8B-Instruct-Unredacted-MAX is a state-of-the-art and unredacted evolution of the original Qwen3-VL-8B-Instruct model, carefully fine-tuned using advanced abliterated training strategies that are explicitly designed to reduce or eliminate internal refusal mechanisms which typically restrict the output of conventional vision-language models, while simultaneously preserving and enhancing the model’s intrinsic multimodal reasoning and instruction-following capabilities; as an 8-billion-parameter system, it is capable of understanding and processing highly complex visual inputs and producing unrestricted, richly detailed, contextually nuanced captions, explanations, and analyses across a wide array of domains including artistic, technical, scientific, forensic, and abstract content, enabling use cases such as high-fidelity data annotation, accessibility improvement, creative and narrative storytelling, historical or medical dataset curation, and thorough red-teaming or bias evaluation research, all while balancing computational efficiency, output precision, and interpretability, making it an ideal tool for researchers, developers, and professionals seeking a powerful, unfiltered, and versatile vision-language model that can reason deeply, follow complex instructions, and generate highly descriptive, human-like responses across diverse multimodal tasks.
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## Qwen3-VL-8B-Instruct-Unredacted-MAX [GGUF]
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| File Name | Quant Type | File Size | File Link |
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| - | - | - | - |
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| Qwen3-VL-8B-Instruct-Unredacted-MAX.BF16.gguf | BF16 | 16.4 GB | [Download](https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX-GGUF/blob/main/Qwen3-VL-8B-Instruct-Unredacted-MAX.BF16.gguf) |
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| Qwen3-VL-8B-Instruct-Unredacted-MAX.Q8_0.gguf | Q8_0 | 8.71 GB | [Download](https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX-GGUF/blob/main/Qwen3-VL-8B-Instruct-Unredacted-MAX.Q8_0.gguf) |
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| Qwen3-VL-8B-Instruct-Unredacted-MAX.mmproj-bf16.gguf | mmproj-bf16 | 1.16 GB | [Download](https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX-GGUF/blob/main/Qwen3-VL-8B-Instruct-Unredacted-MAX.mmproj-bf16.gguf) |
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| Qwen3-VL-8B-Instruct-Unredacted-MAX.mmproj-q8_0.gguf | mmproj-q8_0 | 752 MB | [Download](https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX-GGUF/blob/main/Qwen3-VL-8B-Instruct-Unredacted-MAX.mmproj-q8_0.gguf) |
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## Quants Usage
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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config.json
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{
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"model_type": "qwen3_vl"
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}
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{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}
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