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AI 모델 2119

• MOSS-Image-Text-Encoder-Heretic-DHQ-GGUF↗

  pottokao

  One file does two jobs: it **rewrites** a short idea into a full MOSS-Image caption, then **encodes** that caption for the DiT. Weights are loaded once. A Q4-class file fits an 8 GB card. The quantization is **DHQ** (DiT-aware quantization): every tensor's format is chosen from measured damage on two rulers at once — the error after MOSS-Image's own text stage (what the DiT sees) and the KL of the rewrite — instead of llama.cpp's fixed per-type rules. At the same file size, every DHQ build beats the standard llama.cpp quant on both rulers. Use the **Dual-Head Dragon** ComfyUI node: **https://github.com/pottokao-dotcom/ComfyUI-DualHead-Dragon** (MOSS-Image support: `products/moss.py`).

• EdgeTAM-hf↗

  yonigozlan

  EdgeTAM is an on-device executable variant of the SAM 2 for promptable segmentation and tracking in videos. It runs 22× faster than SAM 2 and achieves 16 FPS on iPhone 15 Pro Max without quantization. EdgeTAM can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline: EdgeTAM's key strength is its ability to track objects across video frames. Here's how to use it for video segmentation:

• NVIDIA-Nemotron-3.5-Bolt-74B-A9B-Abliterated-24GB-GGUF↗

  BoldingBuilds

  An uncensored edit of **NVIDIA Nemotron-3.5-Bolt-74B-A9B**, a hybrid Mamba/MoE model, produced by editing NVIDIA's **shipped 24GB 2-bit GGUF** (nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-GGUF) **directly** — no BF16 requantize at any point. That matters for this model specifically. Bolt is a LatentMoE: every routed expert sums through one dense `ffn_latent_up` per layer, and the experts themselves are ~17GB of IQ2_XXS weights produced by NVIDIA's quantization-aware distillation. A conventional abliterate-then-requantize pipeline would throw that QAD work away. Instead the projection is applied in place and codes are re-picked under the **original block scales**, so at λ=0 the output file is byte-identical to the input by construction — which is the self-test the build gates on (verified: all edited tensors byte-identical at λ=0). The published file is the same length as NVIDIA's, 22,018,326,528 bytes. Bolt is multimodal, so the projector ships alongside. The edit touched only text-model tensors, so the projector is byte-for-byte NVIDIA's file; download both or vision silently does nothing.

• Hentai-NSFW-LoRA-v3↗

  perpetual3x

  Let's be honest: I know what many of you were hoping to see when you flipped through manga—and it was not the table of contents. Qwen Image 2512 can now turn that long-standing wish into pixels. Apparently dreams do come true; they just need a LoRA, a sufficiently shameless prompt, and 96GB of VRAM. Some datasets choose one style and stay there. This one changed artists, decades, proportions, line weight, and rendering habits before breakfast. Training it felt less like teaching a single illustrator and more like trying to keep an entire convention moving in roughly the same direction. > **Important Anatomy Limitation:** The training dataset contains a very wide > range of visual styles and anatomical conventions. Because the style changes > so substantially from image to image, this LoRA does **not** guarantee > anatomical accuracy. It may reproduce exaggerated proportions, inconsistent > structures, or anatomy that follows a particular drawing style rather than > physical reality. Consider this a documented limitation, not a surprise > anatomy expansion pack.

• sam-hq-vit-huge↗

  syscv-community

• Baichuan2-7B-Chat↗

  baichuan-inc

  - 📖 模型介绍/Introduction - ⚙️ 快速开始/Quick Start - 📊 Benchmark评估/Benchmark Evaluation - 👥 社区与生态/Community - 📜 声明与协议/Terms and Conditions Baichuan 2 是[百川智能]推出的新一代开源大语言模型，采用 **2.6 万亿** Tokens 的高质量语料训练，在权威的中文和英文 benchmark 上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits 量化，所有版本不仅对学术研究完全开放，开发者也仅需[邮件申请]并获得官方商用许可后，即可以免费商用。具体发布版本和下载见下表： Baichuan 2 is the new generation of large-scale open-source language models launched by Baichuan Intelligence inc.. It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size. This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through email request. The specific release versions and download links are listed in the table below:

• wav2vec2-large-xlsr-53↗

  facebook

  The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out this blog for more information. Authors: Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli **Abstract** This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.

• MiniMax-H3-GGUF↗

  joeygambino

  GGUF quantizations of MiniMax-H3's 33B video+audio DiTs, quantized from the Comfy-Org bf16 repackages. Runs in ComfyUI with ComfyUI-GGUF plus a one-line architecture patch (node pack: **ComfyUI-H3-Multishot**; workflows: **MiniMax-H3-Multishot-Workflow**). > **Want smaller files?** There is a second set built from MiniMax's pruned > checkpoints, ~40% smaller at the same quant tier, in > **MiniMax-H3-curve-GGUF**. > They need **ComfyUI 0.30.0+**, and they are a different trade, not a straight > replacement - so this repo stays exactly as it is. Stay here if you are on an > older ComfyUI, or if you would rather run the larger quants. `fl2va` = text/first-last-frame to video+audio (T2V and I2V). `ref2va` = reference-conditioned generation (identity from up to 9 images, 3 videos with soundtracks, 3 voice clips).

• LTXV-13B-0.9.8-dev-GGUF↗

  QuantStack

  The model files can be used in ComfyUI with the ComfyUI-GGUF custom node. Place the required model(s) in the following folders: The old example workflow from last version should still work, youll just need to switch the vae and the gguf. **Example workflow** - based on the official example workflow *As this is a quantized model not a finetune, all the same restrictions/original license terms still apply.*

• Weyaxi_-_ChatAYT-Lora-Assamble-Marcoroni-gguf↗

  RichardErkhov