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AI Models 2119

• 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+.

• bigvgan_v2_44khz_128band_512x↗

  NVIDIA

  - **Jul 2024 (v2.3):** - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio. - **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.

• Yi-6B↗

  01-ai

  If you're interested in Yi's adoption of Llama architecture and license usage policy, see Yi's relation with Llama. ⬇️ > 💡 TL;DR > > The Yi series models adopt the same model architecture as Llama but are **NOT** derivatives of Llama. 🔥 2024-07-29 : The Yi Cookbook 1.0 is released, featuring tutorials and examples in both Chinese and English.

• Depth-Anything-V2-Metric-Indoor-Small-hf↗

  depth-anything

  This model represents a fine-tuned version of Depth Anything V2 for indoor metric depth estimation using the synthetic Hypersim datasets. Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release but employs synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. This fine-tuned version for metric depth estimation was first released in this repository. **Six metric depth models** of three scales for indoor and outdoor scenes, respectively, were released and are available:

• chatterbox↗

  ResembleAI

  **Chatterbox Multilingual V3** is the latest general-purpose multilingual TTS model in the Chatterbox family. It keeps the same 0.5B model size while improving speaker similarity, reducing hallucinations, and producing more natural, conversational speech across languages. V3 is designed for broad language coverage like V2, but with stronger stability and more expressive generation. It is the recommended multilingual model for users who want one voice cloning model that works across many languages. Try it in the Chatterbox Multilingual TTS V3 Space. Alongside V3, we are releasing the **Single Language Pack**: dedicated finetunes for priority languages where tighter quality control, stronger language-specific behavior, and more specialized speech generation are valuable.

• Clef-GGUF↗

  abenzerps

  GGUF quantizations of Cloudflare/clef, a 27B multimodal decision model post-trained from Qwen3.8-27B. For image or video input, use the included multimodal projector (`clef-mmproj-BF16.gguf`). Tested with `torch` 2.11 and `transformers` 5.10.2 on a single H200. Image and video inputs also need `pillow`. `systemone` takes a Jev/SystemOne `POST /v1/systemone` request body and returns the same response body: `model`, `answers` keyed by question ID, and `usage`. A `choice` answer has `choice`, `confidence`, and `probabilities`; a `score` answer has the expected `score`, `confidence`, `legend`, and `probabilities`; a `noul` answer has the probability of true. `instructions` is optional, and `images` and `videos` may be added to the request.