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AI モデル 183

  • Minimax-H3-Quantfunc-4bit↗

    QuantFunc

    🌐 Website  |  🐙 GitHub  |  🤗 Hugging Face  |  🤖 ModelScope  |  🎨 Civitai  |  🎮 Discord QuantFunc INT4 cuts MiniMax H3's core weight precision from 16-bit to 4-bit. Character detail, style fidelity and fast motion stay clear and coherent, while H3's native video+audio generation is fully preserved. In our internal FL2VA evaluation, QuantFunc INT4 vs the BF16 baseline (same prompt, same seed) measures ~**23.7 dB PSNR**.

  • Hy3-GGUF↗

    AngelSlim

    Dedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit. 📖 Documentation &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp💬 WeChat Low-bit quantized **Hy3 (hy_v3)** GGUFs for llama.cpp — ready to run out of the box, with MTP self-speculative decoding. This repo ships the quantized models, plus the mixed-precision recipes to build your own from a calibration set.

  • SCOPED-8B-GGUF↗

    mradermacher

  • Ming-flash-omni-2.0↗

    inclusionAI

    The newly released Ming-flash-omni 2.0 leverages the Ling-2.0 architecture—a Mixture-of-Experts (MoE) framework comprising 100B total and 6B active parameters. Representing a generational advancement over its predecessor, it establishes new State-of-the-Art (SOTA) benchmarks among open-source omni-MLLMs. Ming-flash-omni 2.0 effectively synergizes foundational abilities with specialized domain expertise. In particular, it exhibits superior performance in visual encyclopedic knowledge, immersive speech synthesis, and high-dynamic image generation and manipulation. Compared to Ming-flash-omni Preview, Ming-flash-omni 2.0 focuses on optimizing capabilities across the following key domains: - **Expert-level Multimodal Cognition**: It accurately identifies plants and animals, recognizing cultural references (from regional cuisines to global landmarks), and delivering expert-level analysis of artifacts, including era, form, and craftsmanship. By synergizing high-resolution visual capture with a vast knowledge graph, the model achieves "vision-to-knowledge" synthesis, enabling superior knowledge understanding. You can download our latest model from both Huggingface and ModelScope. For previous version model like Ming-flash-omni-Preview, Please refer to this link.

  • Ornith-1.0-35B-GGUF-llamacpp-tp1↗

    LordNeel

    Single-GPU llama.cpp GGUF package for `deepreinforce-ai/Ornith-1.0-35B`. The supported serving policy is `tp=1` — one model copy per GPU. Multi-GPU tensor-parallel serving is intentionally out of scope for this version. This release ships six body quants (Q3_K_M → Q8_0) plus an **integrated IQ4_XS-MTP graft** that adds a native multi-token-prediction (MTP) draft head for low-concurrency speculative decode. Quant quality is measured against the upstream BF16 GGUF with a native llama.cpp next-token top-64 KL-divergence probe over 32 coding prompts. > **MTP graft note:** active MTP is byte-for-byte identical to target-only at the > next-token (API-visible) level, and gives ~1.3× single-stream decode. It is > **not** bit-exact to target-only over long deterministic sequences — see > MTP. Use target-only for strict reproduction, > MTP for throughput on open-ended work.

  • Qwen-72B↗

    阿里巴巴 · Qwen

    🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Paper &nbsp&nbsp ｜ &nbsp&nbsp🖥️ Demo **通义千问-72B**（**Qwen-72B**）是阿里云研发的通义千问大模型系列的720亿参数规模的模型。Qwen-72B是基于Transformer的大语言模型, 在超大规模的预训练数据上进行训练得到。预训练数据类型多样，覆盖广泛，包括大量网络文本、专业书籍、代码等。同时，在Qwen-72B的基础上，我们使用对齐机制打造了基于大语言模型的AI助手Qwen-72B-Chat。本仓库为Qwen-72B的仓库。 1. **大规模高质量训练语料**：使用超过3万亿tokens的数据进行预训练，包含高质量中、英、多语言、代码、数学等数据，涵盖通用及专业领域的训练语料。通过大量对比实验对预训练语料分布进行了优化。 2. **强大的性能**：Qwen-72B在多个中英文下游评测任务上（涵盖常识推理、代码、数学、翻译等），效果显著超越现有的开源模型。具体评测结果请详见下文。 3. **覆盖更全面的词表**：相比目前以中英词表为主的开源模型，Qwen-72B使用了约15万大小的词表。该词表对多语言更加友好，方便用户在不扩展词表的情况下对部分语种进行能力增强和扩展。 4. **较长的上下文支持**：Qwen-72B支持32k的上下文长度。

  • gemma-scope-2b-pt-mlp↗

    Google

  • Qwen-7B-Chat↗

    阿里巴巴 · Qwen

    🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Paper &nbsp&nbsp ｜ &nbsp&nbsp🖥️ Demo **通义千问-7B（Qwen-7B）**是阿里云研发的通义千问大模型系列的70亿参数规模的模型。Qwen-7B是基于Transformer的大语言模型, 在超大规模的预训练数据上进行训练得到。预训练数据类型多样，覆盖广泛，包括大量网络文本、专业书籍、代码等。同时，在Qwen-7B的基础上，我们使用对齐机制打造了基于大语言模型的AI助手Qwen-7B-Chat。相较于最初开源的Qwen-7B模型，我们现已将预训练模型和Chat模型更新到效果更优的版本。本仓库为Qwen-7B-Chat的仓库。 **Qwen-7B** is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, we release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. Now we have updated both our pretrained and chat models with better performances. This repository is the one for Qwen-7B-Chat.

  • gemma-scope-9b-pt-res↗

    Google

  • Ming-omni-tts-0.5B↗

    inclusionAI

    🌐 Project Page ｜🤗 Hugging Face ｜ 🤖 ModelScope | 🎮 Gradio Demo-zh | 🎮 Gradio Demo-en | 💬 DingTalk(钉钉) Ming-omni-tts is a high-performance unified audio generation model that achieves precise control over speech attributes and enables single-channel synthesis of speech, environmental sounds, and music. Powered by a custom 12.5Hz continuous tokenizer and Patch-by-Patch compression, it delivers competitive inference efficiency (3.1Hz). Additionally, the model features robust text normalization capabilities for the accurate and natural narration of complex mathematical and chemical expressions. - 🔊 **Fine-grained Vocal Control:** The model supports precise control over speech rate, pitch, volume, emotion, and dialect through simple commands. Notably, its accuracy for Cantonese dialect control is as high as **93%**, and its emotion control accuracy reaches **46.7%**, surpassing CosyVoice3. - 🌌 **Intelligent Voice Design:** Features 100+ premium built-in voices and supports zero-shot voice design through natural language descriptions. Its performance on the Instruct-TTS-Eval-zh benchmark is on par with Qwen3-TTS. - 🎶 **Immersive Unified Generation:** The industry’s first autoregressive model to jointly generate speech, ambient sound, and music in a single channel. Built on a custom 12.5Hz continuous tokenizer and a DiT head architecture, it delivers a seamless, "in-the-scene" auditory experience. - ⚡ **High-efficiency Inference:** Introduces a "Patch-by-Patch" compression strategy that reduces the LLM inference frame rate to 3.1Hz. This significantly cuts latency and enables podcast-style audio generation while preserving naturalness and audio detail. - 🧪 **Professional Text Normalization:** The model accurately parses and narrates complex formats, including mathematical expressions and chemical equations, ensuring natural-sounding output for specialized applications.