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GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. These GGUF files contain the Qwen-Image-2.1 image transformer; the original text encoder and VAE are also required for inference. 1. Download one GGUF file from this repository. 2. Place it in the ComfyUI diffusion models directory. 3. Download the required text encoder and VAE from Qwen/Qwen-Image-2.1. 4. Place them in their corresponding ComfyUI model directories. 5. Load a Qwen-Image-2.1 workflow and select the GGUF model.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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GSQ-RCO upstream · layout preserved 144 tensors transplanted IQ3_S / IQ3_XXS / IQ2_XS / Q2_0 per-tensor blake2b verified 262K context APACHE-2.0 Qwen3.8-Flash-Next · GSQ-RCO-abliterated The refusal direction removed from an already-quantized GSQ-RCO model — 144 tensors across all 48 layers swapped for ready-made abliterated weights, with every value and scale GSQ learned left untouched and the upstream per-tensor type assignment restored exactly . The IQ3_S build is verified tensor by tensor: everything that changed was supposed to, nothing else moved. 🧭 What this repository is An abliterated build of the official ISTA-DASLab GSQ-RCO quantizations . Not a single one of GSQ's learned quantized tensors was recomputed — 144 "write-to-residual-stream" projection tensors across all 48 layers were replaced with the corresponding tensors from a ready-made abliterated release. Base ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF — GSQ learned quantization + RCO budget-constrained type assignment Ablation source orcarouter/Qwen3.8-Flash-Next-Uncensored-GGUF — the 144 target tensors are taken straight from it Method Byte-level GGUF → GGUF tensor transplant (135 of 144 targets); the 9 remaining expert tensors, which upstream stores as Q2_0, are re-encoded from the abliterated values using GSQ's own learned block scales — no GSQ value is recomputed, only the codes Not done Any dequantize → quantize cycle; any re-quantization from BF16 / safetensors In one line: what you get is still the GSQ-RCO quantization you already know , with those 144 tensors abliterated. It is the only abliteration route that preserves GSQ's work. ⚠️ Safety notice This model is abliterated and refusal-removed — it has no meaningful built-in guardrails and may comply with harmful, illegal or unsafe requests. Deploy it only where you can supply your own moderation, access control and legal review; do not put it in front of end users without a safety layer of your own. 本模型经过拒答方向消融，不具备可靠的内置安全护栏，可能配合有害、违法或不安全的请求。请仅在你能够提供审核、访问控制与法律审查的场景部署。

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This model accepts as input lower-cased, unpunctuated, unsegmented text in 47 languages and performs punctuation restoration, true-casing (capitalization), and sentence boundary detection (segmentation). All languages are processed with the same algorithm with no need for language tags or language-specific branches in the graph. This includes continuous-script and non-continuous script languages, predicting language-specific punctuation, etc. This model is fun to play with, but the results could be better. I would recommend these newer, better models: * Better English model * Better Romance languages model * Better 47-language

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This is an `xlm-roberta` fine-tuned to restore punctuation, true-case (capitalize), and detect sentence boundaries (full stops) in 47 languages. If you want to just play with the model, the widget on this page will suffice. To use the model offline, the following snippets show how to use the model both with a wrapper (that I wrote, available from `PyPI`) and manual usuage (using the ONNX and SentencePiece models in this repo). The easiest way to use this model is to install punctuators:

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充