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AI 모델 2119

• wav2vec2-large-xlsr-gu↗

  gchhablani

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Gujarati using the OpenSLR SLR78 dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows, assuming you have a dataset with Gujarati `sentence` and `path` fields: 90% of the OpenSLR Gujarati Male+Female dataset was used for training, after removing few examples that contained Roman characters. The colab notebook used for training can be found here.

• stable-diffusion-xl-base-1.0-GGUF↗

  gpustack

  **Model creator**: Stability AI **Original model**: stable-diffusion-xl-base-1.0 **GGUF quantization**: based on stable-diffusion.cpp ac54e that patched by llama-box. **VAE From**: madebyollin/sdxl-vae-fp16-fix. SDXL consists of an ensemble of experts pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module. Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations.

• resnet101.a1_in1k↗

  timm

  This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Recipe details: * ResNet Strikes Back `A1` recipe * LAMB optimizer with BCE loss * Cosine LR schedule with warmup - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 44.5 - GMACs: 7.8 - Activations (M): 16.2 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/huggingface/pytorch-image-models

• whisper-base.en-gguf↗

  handy-computer

  Ported from upstream commit 911407f, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper base.en — converted to GGUF for transcribe.cpp. English-only; faster than the multilingual model at the same size. Encoder-decoder transformer; 30-second windows with chunked long-form decoding. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.

• chronos-bolt-small↗

  amazon

  🚀 **Update Feb 14, 2025**: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.

• distilbert-nsfw-text-classifier↗

  eliasalbouzidi

  This model is designed to categorize text into two classes: "safe", or "nsfw" (not safe for work), which makes it suitable for content moderation and filtering applications. The model was trained using a dataset containing 190,000 labeled text samples, distributed among the two classes of "safe" and "nsfw". To improve the performance of the model, it is necessary to preprocess the input text. You can refer to the preprocess function in the app.py file in the following space: .

• SmolLM-1.7B↗

  HuggingFaceTB

  1. Model Summary 2. Limitations 3. Training 4. License 5. Citation SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post. While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post.

• Qwen-Image-2.1-Fun-Controlnet-Union↗

  alibaba-pai

  Qwen-Image-2.1-Fun-Controlnet-Union is a **ControlNet-Union branch for Qwen-Image 2.1** (the flow-matching text-to-image DiT). A single checkpoint drives **8 structural control conditions** (Canny, Depth, Grayscale, HED, Lineart, MLSD, Pose, Scribble) *and* image inpainting, without per-condition weights. The checkpoint holds **only the control branch** (`control_img_in` plus 16 `control_blocks`, about 7.0 GB) and is loaded on top of the base Qwen-Image 2.1 transformer. - **Union control over 8 conditions**: one checkpoint handles Canny, Depth, Grayscale, HED, Lineart, MLSD, Pose and Scribble control images for text-to-image generation — no per-condition checkpoint switching. - **Dense control injection**: the control branch attaches a skip to every 2nd of the 32 transformer blocks (`control_layers = [0, 2, 4, …, 30]`, 16 injection points). Each control skip is added back to the main branch through zero-gated `before_proj` / `after_proj` projections, giving tight structural adherence while the base model stays frozen. - **Control and inpainting share one branch**: the control input is widened to `control_in_dim = 129` — `control latents (64) | mask (1) | masked-image latents (64)`. For pure control the mask / masked-image channels are zero-padded; for inpainting the same branch re-draws the masked region from the prompt. The two can also be **combined** — a control image and a mask are fed together, so the re-drawn region follows both the prompt and the given structure. - **CFG-distilled fast sampling**: the standalone example scripts run with `guidance_scale = 1.0` (single forward pass per step, no classifier-free guidance needed). - `control_context_scale` scales every control skip before it is added to the main branch: `1.0` is the strongest control (used for all results below), lower values weaken the guidance, `0.0` switches the control branch off. - **Prompt-friendly**: write a prompt that describes the **whole target image**; the masked region is conveyed by the mask channel, not by the text. Detailed prompts give better stability. - Qwen-Image 2.1 encodes the prompt (and any condition image) with a **Qwen3-VL** text encoder + processor, and its VAE decodes to **RGBA**, so every preview is saved as PNG. Any ordinary RGB control image at the target canvas works; the model tolerates different line thickness, thresholds and crops.

• wav2vec2-large-xlsr-53-arabic↗

  jonatasgrosman

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the train and validation splits of Common Voice 6.1 and Arabic Speech Corpus. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

• ModernBERT-base-zeroshot-v2.0↗

  MoritzLaurer

  This model is answerdotai/ModernBERT-base fine-tuned on the same dataset mix as the `zeroshot-v2.0` models in the Zeroshot Classifiers Collection. The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 2