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AI 模型 2119

• unispeech-sat-base-plus-sv↗

  Microsoft

  The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu **Abstract** *Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..*

• Samantha-1.11-7b-i1-GGUF↗

  mradermacher

• MiniCPM5-1B-GGUF↗

  openbmb

  MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | MiniCPM Desk Pet | Online Demo We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA. 🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.

• Qwen3.8-27B-Uncensored-FIT-GGUF↗

  SC117

  FIT-GGUF v0.1 ZERO-BYTE VERIFIED 14 TIERS · 7–13.5 GiB MTP HEAD REMOVED MEASURED KL + SAME-TOP APACHE-2.0 Qwen3.8-27B-Uncensored · FIT-GGUF Fourteen continuous-size GGUF quantizations — every file exactly its predicted size, every tier measured. 🧭 About FIT-GGUF — the tool behind these files Every file in this repository was planned, executed and verified by FIT-GGUF , an open-source, deterministic tensor-level planning layer on top of llama.cpp quantization presets. Standard GGUF quantization asks you to pick one of a handful of presets; FIT-GGUF starts from the largest supported preset below your requested byte budget, then spends the remaining bytes on deterministic tensor-level precision upgrades. Traditional GGUF gives you presets. FIT gives you a size slider. Deterministic size prediction & recipe execution ✅ Validated Universally optimal tensor allocation ⚠️ Not established — FIT claims precise size control, not a universal quality optimum The method, the full preregistered research record and the fit CLI are open source: github.com/Scorp1o117/FIT-GGUF ⚠️ Safety notice / 安全提示 The source model is an abliterated, refusal-removed model with no meaningful built-in guardrails, and may comply with harmful, illegal or unsafe requests. Use it only where you can provide appropriate moderation, access control and legal review. Do not deploy it to end users without your own safety layer. 源模型经过拒答方向移除，不具备可靠的内置安全护栏；请仅在合法、受控、具备审核与滥用防护的环境中使用，使用者自行承担部署责任。

• ZeroTTS↗

  zeroweight-ai

  **The most accurate open Vietnamese TTS we know of — 4× fewer word errors than the next best model**, and it runs faster than real time on a laptop CPU. Every system reads **raw text** — dates, numbers and acronyms verbatim, exactly as they appear in the wild, with no text frontend in front of the model. **4× fewer word errors than the next-best system**, and the fastest of the four on CPU. The gap is much wider in latency than in throughput: the two XTTS fine-tunes also beat real time (0.71×) but need seconds to emit their first sample, while OmniVoice is 6× *slower* than real time. All three are sized and tuned for a GPU, and it shows.

• Gemma2-9b-Neuro-sama-Q4_K_M-GGUF↗

  neifuisan

• Cosmos-Transfer1-7B-Sample-AV-Single2MultiView↗

  NVIDIA

• drama3-expressive-medium-GGUF↗

  pentacoxian-dev

  **Built From Fish Audio.** This is a fine-tuned and quantized derivative of Fish Audio Drama 3. Fish Audio does not sponsor or endorse it. Drama 3 follows an acting direction at the start of a line, but it mostly ignores one placed in the middle: `[calm] It's fine, really. [voice breaking] I just miss her.` tends to stay calm, or to go silent at the second direction. drama3-expressive-medium was fine-tuned to change delivery where the direction says. It also speaks the whole line and ends cleanly far more often. It keeps all 256 experts and runs in 24 GB of VRAM. No reference audio: each sample's seed makes up its own voice, speaking the line's language (`--task tts` with no `voice_ref`). The first table keeps the words fixed and changes only the opening direction; the second changes the delivery mid-line.

• GLM-5.2-NVFP4↗

  NVIDIA

  The NVIDIA GLM-5.2 NVFP4 model is the quantized version of ZAI’s GLM-5.2 model, which is an auto-regressive language model that uses an optimized transformer architecture. GLM-5.2 is a Mixture-of-Experts (MoE) model for reasoning and coding that uses sparse attention (with an IndexShare indexer) to support a long context. For more information, please check here. The NVIDIA GLM-5.2 NVFP4 model is quantized with Model Optimizer. **GOVERNING TERMS:** Use of the model is governed by the MIT License, same as the base model. Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.

• rtdetr_v2_r18vd↗

  PekingU

  The RT-DETRv2 model was proposed in RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu. RT-DETRv2 refines RT-DETR by introducing selective multi-scale feature extraction, a discrete sampling operator for broader deployment compatibility, and improved training strategies like dynamic data augmentation and scale-adaptive hyperparameters. These changes enhance flexibility and practicality while maintaining real-time performance. This model was contributed by @jadechoghari with the help of @cyrilvallez and @qubvel-hf RT-DETRv2 consistently outperforms its predecessor across all model sizes while maintaining the same real-time speeds.