전체 검색

AI 모델 2119

• muscriptor-medium↗

  cocktailpeanut

  **MuScriptor** is an open-weight model for **general-purpose, multi-instrument automatic music transcription (AMT)**: it converts a music recording (any genre, multiple simultaneous instruments) into a stream of notes played. This repository hosts the **medium** variant (≈300M parameters), the default checkpoint downloaded by the `muscriptor` library. `muscriptor-medium` balances quality and footprint. For the best transcription quality use `muscriptor-large` (≈1.3B); for the smallest/fastest option use `muscriptor-small` (≈100M). `load_model` accepts a size keyword (`"small"`/`"medium"`/`"large"`), a local `.safetensors` path, or an `hf://` / `https://` URL. Weights loaded by size keyword (or any `hf://` URL) are cached in the standard Hugging Face cache (`~/.cache/huggingface/hub`, configurable via `HF_HOME`); weights fetched from a plain `http(s)://` URL are cached under `~/.cache/muscriptor/`. Input audio can be WAV or any format `libsndfile` reads (mp3, flac, ogg, m4a, …); it is resampled to 16 kHz mono internally.

• jina-embeddings-v2-base-code↗

  jinaai

  The easiest way to starting using `jina-embeddings-v2-base-code` is to use Jina AI's Embedding API. `jina-embeddings-v2-base-code` is an multilingual **embedding model** speaks **English and 30 widely used programming languages**. Same as other jina-embeddings-v2 series, it supports **8192** sequence length. `jina-embeddings-v2-base-code` is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone `jina-bert-v2-base-code` is pretrained on the github-code dataset. The model is further trained on Jina AI's collection of more than 150 millions of coding question answer and docstring source code pairs. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.

• Qwen3.8-27B-GGUF↗

  KikoCis

  Quants of Qwen/Qwen3.8-27B. What this repo adds over the other GGUF builds of the same model is not the files: it is a measured answer to *how far down you can quantize before an agent stops working*, and the honesty about how noisy that measurement is. The two top rungs sit above the 3.40-bpw build that scores 40/40, but they were not put through the benchmark themselves — "not measured" here means exactly that, not "assumed fine". `IQ1_M-compact` is the build that Elffuss Claw loads on WebGPU — one tab, one machine, no install and no sharding across devices — through Elffuss Engine (Apache-2.0), which reads a **standard GGUF over HTTP** by range requests and dequantises inside the shader. It is slow and the first load is several gigabytes; it answers, it does not feel instant. Note the row above: at 1.94 bpw this build is for text, not for driving tools.

• Cosmos-Predict1-7B-Text2World-Sample-AV-Multiview↗

  NVIDIA

• sbert-cased-finnish-paraphrase↗

  TurkuNLP

  Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences can be found here. The same as in the HuggingFace documentation of the English Sentence Transformer. Either through `SentenceTransformer` or `HuggingFace Transformers`

• deepseek-vl-7b-chat↗

  deepseek-community

  This is the transformers version of Deepseek-VL-Hybrid, a foundation model for Visual Language Modeling. Deepseek-VL-Hybrid was introduced by the DeepSeek AI team. It is a vision-language model (VLM) designed to process both text and images for generating contextually relevant responses. The model leverages LLaMA as its text encoder, while SigLip is used for encoding low-resolution images and SAM (Segment Anything Model) is incorporated to handle high-resolution image encoding, enhancing the model’s ability to process fine-grained visual details. Deepseek-VL-Hybrid is a variant of Deepseek-VL that uses SAM (Segment Anything Model) to handle high-resolution image encoding. > We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in r

• hubert-large-ls960-ft↗

  facebook

  The large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. The model is a fine-tuned version of hubert-large-ll60k. Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed

• sam2-hiera-small-hf↗

  facebook

• mms-tts-kan↗

  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.

• bert-base-parsbert-uncased↗

  HooshvareLab

  ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned) This model is pre-trained on a large Persian corpus with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 2M documents. A large subset of this corpus was crawled manually.