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AI 모델 2119

• mms-tts-fas↗

  facebook

  This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.

• Qwen3.8-27B-MTP-4bit↗

  mlx-community

  This repository contains the Multi-Token Prediction (MTP) drafter weights split from `Qwen/Qwen3.8-27B` and quantized with `mlx_vlm.convert` for `mlx-vlm` speculative decoding. This is not a standalone model. Use it as the draft model alongside a compatible Qwen3.8 27B target checkpoint. The adapter holds only the MTP drafter weights; the target supplies the token embeddings and language-model head at runtime. Use an adapter and target derived from the same Qwen3.8 27B checkpoint. The upstream Apache 2.0 license and model limitations continue to apply.

• OrcaMaidXL-17B-32k-GGUF↗

  TheBloke

  GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

• Opus-Samantha-Llama-3-8B-GGUF↗

  mradermacher

• Qari-OCR-v0.3-VL-2B-Instruct↗

  NAMAA-Space

  - QARI-OCR v0.3 is a specialized vision-language model fine-tuned for Arabic Optical Character Recognition with a focus on **structural document understanding**. - Built on Qwen2-VL-2B-Instruct, this model excels at preserving document layouts, HTML tags, and formatting while transcribing Arabic text. - It is described in detail in the paper QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation. - 📐 **Layout-Aware Recognition**: Preserves document structure with HTML/Markdown tags - 🔤 **Full Diacritics Support**: Accurate recognition of tashkeel (Arabic diacritical marks) - 📝 **Multi-Font Handling**: Trained on 12 diverse Arabic fonts (14px-100px) - 🎯 **Structure-First Design**: Optimized for documents with headers, body text, and complex layouts - ⚡ **Efficient Training**: Only 11 hours on single GPU with 10k samples - 🖼️ **Robust Performance**: Handles low-resolution and degraded images While QARI v0.2 achieves better raw text accuracy (CER: 0.061), QARI v0.3 excels in: - ✅ **HTML/Markdown structure preservation** - ✅ **Document layout understanding** - ✅ **Handwritten text recognition** (initial capabilities) - ✅ **5x faster training** than v0.2

• MiniCPM-V-4.7-35B-A3B-GPTQ↗

  openbmb

  > **This repository hosts the GPTQ (W4A16, compressed-tensors, llm-compressor) quantized version of MiniCPM-V 4.7 35B-A3B.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.7-35B-A3B. **MiniCPM-V 4.7** is the latest model in the MiniCPM-V series, available in a **35B-A3B** variant, with **35B-A3B being the first MoE model in the MiniCPM-V series**. It builds on the family's single-image, multi-image, and video understanding capabilities, combining stronger foundation capabilities with efficient visual encoding and on-device inference. Notable features of MiniCPM-V 4.7 include: - 📱 **The First Open-Source MoE Model with Lossless, Smooth On-Device Inference.** With our **Lookahead Expert Scheduling (LES)** technique introduced alongside MiniCPM-V 4.7, combined with fine-grained layer-wise quantization and DSpark speculative sampling integration, MiniCPM-V 4.7 35B-A3B becomes the **first open-source MoE model to run smoothly on-device without compromising performance**, achieving a decoding speed of **13+ tokens/s with just 3 GB of memory on iPhone 17 Pro Max**. - 🔥 **A New Pareto Frontier in Performance and Efficiency.** As model capabilities continue to advance, inference efficiency has become an important measure of a model's value. On resource-constrained devices, delivering the strongest possible capabilities within a limited resource budget calls for higher capability density. The **35B-A3B** variant of MiniCPM-V 4.7 advances the performance–efficiency Pareto frontier for MLLMs. In Instruct mode, 35B-A3B **achieves a higher overall score in less inference time than Qwen3.6 35B-A3B**. - 🚀 **Efficient Architecture.** Based on the intra-ViT early compression technique from LLaVA-UHD v4, MiniCPM-V 4.7 compresses visual tokens early within the vision encoder, reducing computation in subsequent encoder layers. It also supports **mixed 4x/16x visual token compression**, allowing developers to balance visual detail and inference cost across tasks. - 💻 **Broad Platform and Chip Support.** MiniCPM-V 4.7 supports **iOS, Android, and HarmonyOS**, together with open-source on-device deployment support. It also provides **day-0 support for chip platforms from Rockchip, Houmo, Intel, AMD, and Arm**, extending deployment options across mobile and local computing devices. - 🛠️ **Developer Friendly.** MiniCPM-V 4.7 supports i

• Qwen3.8-27B-GPTQ-W4A16↗

  pearsonkyle

  📦 18.1 GiB · 2.8× smaller compressed-tensors · int4 g128 sym 🧬 calibrated at ctx 32,768 ⚡ MTP bundled · 1.71× decode 👁️ Text + Image · vision 3/3 🏅 tool-calling = bf16, turn for turn 🏗️ vLLM 0.27.1 ⚡ What this is A compressed-tensors W4A16 (GPTQ, int4 group-128 symmetric) quantization of Qwen/Qwen3.8-27B that vLLM serves directly — no conversion, no custom runtime, quantization auto-detected from config.json . Calibrated on 4.26M tokens of real agentic-coding sessions packed at 32K context. Sibling of the GGUF ladder , same corpus: this one for vLLM , the GGUF for llama.cpp / Ollama / LM Studio. ⚡ Speculative decoding + vision, both bundled The trained MTP draft head ships inside the checkpoint — one --speculative-config flag, no second file, 1.71× decode at 71.2% acceptance . The vision tower ships too, kept at bf16: shown a synthetic test image it named the colour, shape and position of all three shapes correctly, unprompted detail included ("inverted triangle, base horizontal at the top"). Both are additive — drop the flags and you are back to the identical text model.

• pothole-yolov8↗

  Samdutse

• Ling-3.0-flash-VL-MTP-ROCmFP4-GGUF↗

  kingjones777

  `inclusionAI/Ling-3.0-flash-VL` (124B total / 5.5B active, native multimodal) quantized to ROCmFP4 for AMD Strix Halo, **with a multi-token-prediction head grafted in** from its text sibling — and with the server fix that lets **MTP and images work at the same time**, which they do not in stock llama.cpp. Everything below was measured on the shipped files on one Ryzen AI Max+ 395 (Radeon 8060S, 128 GiB unified). Numbers come from the built GGUFs and from server logs, never from estimates. ⚠️ **COHERENT is the worst of the three on this model** even though it is the largest and the highest bit-width (4.70 bpw vs 4.38). Its PPL is 0.80 worse than STRIX_LEAN — roughly 3.5σ against the ±0.23 error bars — and it is slower on both prefill and decode. It ships for completeness; it is not the "quality" option here. Tier ranking is model-dependent and has to be measured, not assumed.

• mlfoundations-dev_-_mistral_alpaca_sft_sample_7-gguf↗

  RichardErkhov