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AI モデル 2119

• wav2vec2-xls-r-300m_phone-mfa_korean↗

  slplab

  This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on a phonetically balanced native Korean read-speech corpus. * Model Management by: excalibur12 Training Data - Data Name: Phonetically Balanced Native Korean Read-speech Corpus - Num. of Samples: 54,000 (540 speakers) - Audio Length: 108 Hours Evaluation Data - Data Name: Phonetically Balanced Native Korean Read-speech Corpus - Num. of Samples: 6,000 (60 speakers) - Audio Length: 12 Hours

• NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF↗

  AtomicChat

  Atomic Dynamic (AD) quants of NVIDIA's Nemotron 3.5 Bolt E4B, built from NVIDIA's original BF16 weights. From 4.0 GB to 8.0 GB on disk. Bolt E4B is NVIDIA's small reasoning and chat model, post-trained from Google's Gemma-4-E4B-it . You can now run Nemotron 3.5 Bolt E4B, NVIDIA's on-device model, in Atomic Chat . Every file here was measured against the BF16 original on one stand, next to the other publishers' files. > [!NOTE] > **Built on release day, 23 October.** Every `AD-` file below `AD-Q6_K` was > rebuilt the same evening with a better layout (below). `AD-Q4_K_M` uses our own > importance matrix, collected on the BF16 weights over a corpus rendered through > this model's chat template (`imatrix/imatrix.gguf` in the metrics repo). The > other files use Unsloth's published one (`imatrix_unsloth.gguf` from > unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF): > we built `AD-Q5_K_M` to `AD-IQ3_S` with both, and on held-out code the two > came out within noise of each other, slightly ahead with Unsloth's at 3 bits. > `AD-IQ3_XXS` (3.82 GB) was taken down the same evening: it picked the > original's next word only 74.6% of the time, below the 75% we publish at.

• musicgen-medium↗

  facebook

  MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in Simple and Controllable Music Generation by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*. Four checkpoints are released: - small - **medium** (this checkpoint) - large - melody

• musicgen-stereo-medium↗

  facebook

  We further release a set of stereophonic capable models. Those were fine tuned for 200k updates starting from the mono models. The training data is otherwise identical and capabilities and limitations are shared with the base modes. The stereo models work by getting 2 streams of tokens from the EnCodec model, and interleaving those using the delay pattern. Stereophonic sound, also known as stereo, is a technique used to reproduce sound with depth and direction. It uses two separate audio channels played through speakers (or headphones), which creates the impression of sound coming from multiple directions. MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.

• MiniMax-VL-01↗

  MiniMaxAI

  We are delighted to introduce our **MiniMax-VL-01** model. It adopts the "ViT-MLP-LLM" framework, which is a commonly used technique in the field of multimodal large language models. The model is initialized and trained with three key parts: a 303-million-parameter Vision Transformer (ViT) for visual encoding, a randomly initialized two-layer MLP projector for image adaptation, and the MiniMax-Text-01 as the base LLM. MiniMax-VL-01 has a notable dynamic resolution feature. Input images are resized per a pre-set grid, with resolutions from 336×336 to 2016×2016, keeping a 336×336 thumbnail. The resized images are split into non-overlapping patches of the same size. These patches and the thumbnail are encoded separately and then combined for a full image representation. The training data for MiniMax-VL-01 consists of caption, description, and instruction data. The Vision Transformer (ViT) is trained on 694 million image-caption pairs from scratch. Across four distinct stages of the training pipeline, a total of 512 billion tokens are processed, leveraging this vast amount of data to endow the model with strong capabilities. Finally, MiniMax-VL-01 has reached top-level performance on multimodal leaderboards, demonstrating its edge and dependability in complex multimodal tasks. For production deployment, we recommend using vLLM to serve MiniMax-VL-01. vLLM provides excellent performance for serving large language models with the following features: 🔥 Outstanding service throughput performance ⚡ Efficient and intelligent memory management 📦 Powerful batch request processing capability ⚙️ Deeply optimized underlying performance For detailed deployment instructions, please refer to our vLLM Deployment Guide. MiniMax-VL-01 supports Function Calling capability, enabling the model to intelligently identify when external functions need to be called and output parameters in structured JSON format. With Function Calling, you can:

• tts-hifigan-libritts-22050Hz↗

  speechbrain

  This repository provides all the necessary tools for using a HiFIGAN vocoder trained with LibriTTS (with multiple speakers). The sample rate used for the vocoder is 22050 Hz. The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram. Alternatives to this models are the following: - tts-hifigan-libritts-16kHz (same model trained on the same dataset, but for a sample rate of 16000 Hz) - tts-hifigan-ljspeech (same model trained on LJSpeech for a sample rate of 22050 Hz).

• resnet18.fb_swsl_ig1b_ft_in1k↗

  timm

  This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Pretrained on Instagram-1B hashtags dataset using semi-weakly supervised learning and fine-tuned on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 11.7 - GMACs: 1.8 - Activations (M): 2.5 - Image size: 224 x 224 - **Papers:** - Billion-scale semi-supervised learning for image classification: https://arxiv.org/abs/1905.00546 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/facebookresearch/semi-supervised-ImageNet1K-models

• Minimax-H3-T2VA-Veda-8NFE-600Step-Preview↗

  Veda-Sparse

  Don't just take our samples for it. Try your own prompts in the online demo. Available as a custom node on the Comfy Registry; see ComfyUI below. This checkpoint applies to **all generation modalities** supported by MiniMax-H3, including **T2VA, FL2VA, and R2VA**, as well as to **arbitrary numbers of diffusion steps**. The T2VA and 8NFE tags in the repository name only denote the training configuration and do not restrict where the predictor can be used. A dedicated R2VA fine-tuned checkpoint with further improved quality will be released in the near future.

• sam2-hiera-tiny-hf↗

  facebook

• HunyuanImage-3.0-Instruct↗

  腾讯

  👏 Join our WeChat and Discord | 💻 Official website(官网) Try our model! &nbsp&nbsp - **January 26, 2026**: 🚀 **HunyuanImage-3.0-Instruct-Distil** - Distilled checkpoint for efficient deployment (8 steps sampling recommended). - **January 26, 2026**: 🎉 **HunyuanImage-3.0-Instruct** - Release of **Instruct (with reasoning)** for intelligent prompt enhancement and **Image-to-Image** generation for creative editing. - **October 30, 2025**: 🚀 **HunyuanImage-3.0 vLLM Acceleration** - Significantly faster inference with vLLM support. - **September 28, 2025**: 📖 **HunyuanImage-3.0 Technical Report** - Comprehensive technical documentation now available. - **September 28, 2025**: 🎉 **HunyuanImage-3.0 Open Source** - Inference code and model weights publicly available. - HunyuanImage-3.0 (Image Generation Model) - [x] Inference - [x] HunyuanImage-3.0 Checkpoints - [x] HunyuanImage-3.0-Instruct Checkpoints (with reasoning) - [x] vLLM Support - [x] Distilled Checkpoints - [x] Image-to-Image Generation - [ ] Multi-turn Interaction