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AI モデル 2119
- sambanovasystems_-_SambaLingo-Arabic-Chat-70B-gguf↗
RichardErkhov
- Yi-1.5-9B-Chat-16K↗
01-ai
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T
- Ingot-V-27B-Q2↗
dendriteholdings
A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the per-block scales and the norm weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte. Credit belongs to the miner, and to Prism ML for Bonsai 2.
- mms-tts-spa↗
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.
- lab1-cifar10↗
petitoff
Submission branch: `s_109197_103216`. Fine-tuned ImageNet ResNet-18 in Google Colab; only layer4 and fc are updated. The learning rate and epoch are selected by validation accuracy. The test split is used only after model selection. Seed: 42. Samples: train=10000, validation=2000, test=2000. Validation accuracy: 0.8870; test accuracy: 0.8795. Classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. This model recognizes these ten classes and does not detect out-of-distribution images. CIFAR-10 consists of small 32x32 images, so predictions on arbitrary photographs may be unreliable.
- mms-tts-pap↗
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.
- puma_model_checkpoints↗
mhdank
Medical image segmentation project using various U-Net architectures to segment microscopic tissue images from the PUMA dataset. Handles multi-class segmentation for both tissue structures and nuclei types. Contains: - Microscopic tissue images - Coordinate data for segmented regions - Color-coded mask images - Limited samples (addressed via augmentation) python { "background", "nuclei_apoptosis", "nuclei_endothelium", "nuclei_epithelium", "nuclei_histiocyte", "nuclei_lymphocyte", "nuclei_melanophage", "nuclei_neutrophil", "nuclei_plasma_cell", "nuclei_stroma", "nuclei_tumor" } ```
- Kokoro-82M-v1.0-ONNX↗
onnx-community
Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). - Usage - JavaScript - Python - Voices/Samples - Quantizations First, install the `kokoro-js` library from NPM using:
- resnet18.tv_in1k↗
timm
This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample - **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:** - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/pytorch/vision Explore the dataset and runtime metrics of this model in timm model results.
- Wan2.2-TI2V-5B-GGUF↗
unsloth
Every GGUF quantisation of Wan2.2-TI2V-5B that QuantStack/Wan2.2-TI2V-5B-GGUF publishes, plus the companion VAE, mirrored here. Unsloth Studio offers this repo as the curated one-click GGUF pick for Wan2.2 TI2V 5B, so its availability is Studio's problem rather than the repacker's: a rename or a takedown turns the pick into a 404 no client can work around. All 13 quants are mirrored, not a chosen few, because the picker lets you choose the precision. **The weights are unmodified:** byte for byte the files of the same name in the source repo. TI2V-5B is a 720P-only checkpoint: the supported sizes are 1280x704 and 704x1280, and its VAE has temporal compression 4, so valid frame counts are 4k+1.