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AI 모델 2119
- vakyansh-wav2vec2-hindi-him-4200↗
Harveenchadha
Fine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz. **Note: The result from this model is without a language model so you may witness a higher WER in some cases.** This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.
- flux-3-image-klein↗
ml-resources
> Ungated mirror of black-forest-labs/flux-3-image-klein. All credit to Black Forest Labs. This is not an official Black Forest Labs release. The model is licensed under the FLUX Kommunity License (non-commercial), included here as `LICENSE.md` with `NOTICE.md`; any rights to use it are granted directly by Black Forest Labs under that license. FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. This repository holds the distilled checkpoint for generation and image editing. For fine-tuning, LoRA training and research, use FLUX 3 Image Klein Base.
- sam2.1-hiera-tiny-ONNX↗
onnx-community
- Xiaomi-OCR-0-GGUF↗
prithivMLmods
> Xiaomi-OCR-0 is a compact 0.8B OCR-specialized vision-language model based on Qwen3.5-0.8B-Base, designed for unified document parsing and OCR-centric understanding. It is trained on an approximately 170M-sample OCR-focused corpus using Q-Mask text anchoring, continued pretraining, and mixed-task reinforcement learning, enabling document parsing, OCR-based visual question answering, and key information extraction. The model is designed to handle standard, scanned, warped, photographed, and skewed documents, while also supporting structured outputs such as Markdown, OTSL tables, LaTeX formulas, and JSON-based information extraction. **Xiaomi-OCR-0** on Hugging Face — https://huggingface.co/SeerRay-Lab/Xiaomi-OCR-0.
- Carnice-Qwen3.6-MoE-35B-A3B-GGUF↗
samuelcardillo
- Taeh3-Comfy↗
t8star
Kijai原仓库也把2D权重命名为`taeh3.safetensors`，这里**只改文件名、不改张量**，避免覆盖时序版。 已有时序文件请先核对SHA256，不要拿2D版覆盖；这是上游两种不同结构，不可随意互换。 默认选择`taeh3.safetensors`、`phase=low`、每2步更新、最长边256。 要用2D版，在`checkpoint`主动选择`taeh3_2d_kijai.safetensors`。 原sigmas、采样器、步数、最终VAE和声音接线不变；`enabled=false`直接旁路。 预览只观察实际callback的x0开头片段；缓存命中没有新callback时不会制造实时预览。 面板“取消”仅针对绑定的当前请求，不清其他队列、不关闭ComfyUI服务。 预览近似图不代表最终清晰度、整片动作、音频或接缝；2D轮播速度不是源视频帧率。 Merge this repository's `models/` into `ComfyUI/models/`. The default file is the original temporal TAEH3 decoder. Kijai's 2D checkpoint is renamed **without tensor changes** to `taeh3_2d_kijai.safetensors`; explicitly choose it in the T8 preview node. Never overwrite the temporal checkpoint with the 2D model. Insert the MODEL observer before the existing sampler; final VAE/audio/sigmas remain unchanged. Approximate previews are not final videos. MIT applies to the temporal file; Apache-2.0 applies to the 2D file. See the license files.
- Qwen3.8-27B-nvfp4full-dflash2-NInfer-v3↗
kaushikvira
The same bytes you know from kaushikvira/Qwen3.8-27B-nvfp4full-dflash2-NInfer (v2), migrated to the **NInfer v3 artifact container** (upstream `f76e19c0`, model/weight decoupling). Weight tensors are **bit-preserved** — this is a packaging upgrade, not a re-quantization. > ## 🔑 No engine patch needed anymore > > The v2 artifact required a *forked* NInfer engine: the `qwen3.8-27b/nvfp4full` > weights profile had to be registered in C++ (4 files: profile enum, resolver, > binder, workspace cases). **NInfer v3's loader is data-driven** — every weight > binds from the artifact's own logical bindings and per-tensor formats — so > this artifact needs **no model-support patch of any kind**. Any NInfer engine > at upstream v3 (`Neroued/ninfer` master ≥ `f76e19c0`, 2026-09-13) loads and > serves it. Our previous fork's C++ registration commits were deleted during > the v3 rebase and the model still passes the full quality gate (see below), > which is the proof: the engine path in our build *is* upstream's. > > The only fork-local piece left is one allow-list line in the *offline upgrade > tool* (to produce v3 files like this one) — not in the engine, not needed to > serve. Input must match SHA-256 `373dab08…937de1`; output must match `74d2fc17742537defa6897583caaabcc9adfc99bdfbea402ec5e8facd0d5fdfc`.
- nomic-embed-multimodal-3b↗
nomic-ai
`nomic-embed-multimodal-3b` is a dense state-of-the-art multimodal embedding model that excels at visual document retrieval tasks: Nomic Embed Multimodal 3B seamlessly integrates with Retrieval Augmented Generation (RAG) workflows: 1. **Direct Document Embedding**: Skip OCR and complex processing by directly embedding document page images 2. **Faster Processing**: Eliminate preprocessing steps for quicker indexing 3. **More Complete Information**: Capture both textual and visual cues in a single embedding 4. **Simple Implementation**: Use the same API for both text and images
- falcon-40b-tiny-testing↗
BlackSamorez
- Qwen3-4b-Z-Image-Turbo-AbliteratedV1↗
BennyDaBall
This is the **abliterated** version of the Z-Image-Turbo text encoder. I took the **p-e-w heretic method** and hammered it through **1000 trials**, specifically targeting **BOTH** image generation refusals and those peskier general refusals. The result? - **KL Divergence:** A tiny `0.0004` (basically no lobotomy! 🧠✨) - **Refusal Rate:** Only `4/100` in my torture tests. Brought to you by the same chaotic good energy behind: - Z-Image-Engineer-V2.5 - The original Z-Image-Engineer