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AI 模型 2119

• mlfoundations-dev_-_mistral_alpaca_sft_sample_5-gguf↗

  RichardErkhov

• ddpm-cifar10-32↗

  Google

  **Paper**: Denoising Diffusion Probabilistic Models *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* - scheduling_ddpm - scheduling_ddim - scheduling_pndm

• Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF↗

  IsValorum

  If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. > > *Independently computed on private cloud clusters. If this handcrafted release saves you VRAM and runs faster on your GPU, consider fueling the community compute fund on Ko-fi.* > > Explore the new canonical V3 releases. MiniPlus prioritizes Q5-class to near-Q6 fidelity without a fixed file-size ceiling; NanoPlus targets Q4-class quality at a practical footprint. > > - **Qwen3.8 27B — MiniPlus V3** — 14.16 GB **(+13.21% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Qwen3.8 27B — NanoPlus V3** — 11.35 GB **(+11.49% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — MiniPlus V3** — 14.28 GB **(+5.70% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — NanoPlus V3** — 11.08 GB **(+2.26% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > > Percentages compare the reported WikiText-2 PPL with GSQ-RCO IQ3_S (7.07). The linked 27B cards document the evaluation protocol and model variants. > > **Explore the APEX-I MiniPlus V2.1 collection** > > ### EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP > These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do: > > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1** — a versatile frontier MoE for broad reasoning, multilingual work, agents, tool use, and multimodal tasks. > - **Best for:** General reasoning, agent workflows, tool calling, and flexible multimodal use. > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 Abliterated** — the V2.1 refusal-ablated Qwen3.6 edition for users who deliberately prefer reduced refusal behavior. > - **Best for:** Workflows where an abliterated Qwen3.6 variant is explicitly desired. > - **Ornith 1.5 APEX-I-MiniP

• taef1↗

  madebyollin

  TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.

• sam3-litetext-l↗

  vil-uob

• VieNeu-TTS-v3-Turbo↗

  pnnbao-ump

  **VieNeu-TTS v3 Turbo** is the next generation of Vietnamese TTS — **48 kHz** high-fidelity speech, **23 built-in preset voices** across three regions (North / Central / South), **instant voice cloning**, **real-time streaming** with an **OpenAI-compatible API** (16 concurrent streams on one RTX 3060), inline **emotion cues**, and seamless **bilingual (En–Vi) code-switching**. The reference implementation is the **`vieneu` Python SDK (v3.7.1)**. Its minimal install is **torch-free**: on CPU everything runs on **ONNX Runtime** (PyTorch is never imported), and on a CUDA machine it auto-switches to the PyTorch engine with **automatic batching** and a **continuous-batching stream scheduler** — same API, no code change. > [!IMPORTANT] > **What's new in SDK v3.7:** > - **GPU streaming with continuous batching** — `infer_stream` on CUDA runs on one CUDA graph shared by every caller: first audio in **~115 ms**, **16 concurrent streams under 200 ms** on a single RTX 3060 (32 max). > - **OpenAI-compatible streaming API + Docker** — `POST /v1/audio/speech` (`pcm`/`wav`, chunked or SSE) works with the OpenAI SDK, Pipecat, LiveKit; `docker compose --profile api-gpu` / `api-cpu`. Numbers, sizing and CPU-only notes: docs/streaming.md. > - **23 preset voices** covering North / Central / South, both genders and several reading characters (default: `Minh Quân`). > - **LoRA fine-tuning** of v3 Turbo on one consumer GPU. > - Since 3.3: torch-free voice cloning on CPU, optional **int8** CPU backbone (`Vieneu(precision="int8")`, ~2× faster; fp32 is the default), sliding-window repetition penalty.

• Sakura-FLUX.3-Image-Klein-GGUF↗

  webmp3

  **Built with FLUX.** Community quantization of FLUX.3 Image Klein (distilled, 16 steps, no CFG) with the official, unmodified Qwen3-VL-4B-Instruct text encoder. Independent work, not made, reviewed or endorsed by Black Forest Labs. The image model files, the VAE and the workflow are under the **FLUX Kommunity License** (copy in `LICENSE.md`: non-commercial use, or commercial use of outputs only as a Qualifying User under US$5 million revenue with the filtering or review the license requires); the unmodified text encoder is Apache-2.0 (`LICENSE-Apache-2.0.txt`). See `NOTICE`. An uncensored version of the **text encoder** (only the encoder, the image model is not changed) is available separately: Sakura-FLUX.3-Image-Klein-TextEncoder-Uncensored-GGUF. It works with the image model files of this repository. Q8_0 image model and the official text encoder, ComfyUI 0.41.1 with ComfyUI-GGUF (Radeon 8060S), 1024x1024, 16 steps, CFG 1.0, `euler`/`simple`. These are the **same nine prompts and seeds** (1202, 1302, 1305, 1306, 1105, 1103, 1503, 1506, 1406 in the order shown) as in the repositories Sakura-Qwen-Image-2.1-Turbo-GGUF and Sakura-Iris-3B-GGUF, so that the models can be compared. One seed per prompt, no re-rolls, all nine images are shown; the prompts were chosen for those repositories from a larger set, not for FLUX. The prompts are the captions of the images above. The same prompts and seeds with our uncensored text encoder are in Sakura-FLUX.3-Image-Klein-TextEncoder-Uncensored-GGUF. The FLUX images were made with ComfyUI, the Qwen and Iris images with stable-diffusion.cpp, and models, steps and samplers differ, so differences between the sets are not only differences of the text encoder. Everything except the runtime is in this repository. Pick the column of the program you use (🟦 stable-diffusion.cpp, 🟧 ComfyUI):

• mitra-classifier↗

  autogluon

  Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models

• mask2former-swin-base-coco-instance↗

  facebook

  Mask2Former model trained on COCO instance segmentation (base-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.

• Raon-SpeechChat-9B↗

  KRAFTON

  Raon-SpeechChat-9B is a **full-duplex** speech language model that enables real-time, simultaneous listen-and-speak conversation in English. Built on top of Raon-Speech-9B, it extends the base model with full-duplex decoding — the model can listen to a user and generate speech responses at the same time, supporting natural turn-taking, backchannels ("uh-huh", "mm-hmm"), and barge-in handling. Raon-SpeechChat performs strongly on conversational speech capabilities such as pause handling, backchanneling, smooth turn-taking, interruption handling, overlap robustness, and multi-turn dialogue. > **Prerequisites:** NVIDIA GPU with CUDA 12.x (16 GB+ VRAM), Docker, NVIDIA Container Toolkit, and Node.js 18+.