tensor-diet-bf16-gguf
Attoloop基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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vilt_finetuned_200
Atul8827基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwenmodel1st
atypical-chai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗araelectra-base-discriminator
aubmindlab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗aragpt2-base
aubmindlabYou can find more information in our paper AraGPT2 The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API. GPT2-base and medium uses the code from the `gpt2` folder and can trains models from the minimaxir/gpt-2-simple repository. These models were trained using the `lamb` optimizer and follow the same architecture as `gpt2` and are fully compatible with the `transformers` library.
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View details ↗aragpt2-medium
aubmindlab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-base-arabert
aubmindlab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-base-arabertv02
aubmindlab**AraBERT** is an Arabic pretrained language model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the Farasa Segmenter. We evaluate AraBERT models on different downstream tasks and compare them to mBERT), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD
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View details ↗bert-base-arabertv02-twitter
aubmindlabAraBERTv0.2-Twitter-base/large are two new models for Arabic dialects and tweets, trained by continuing the pre-training using the MLM task on ~60M Arabic tweets (filtered from a collection on 100M). The two new models have had emojies added to their vocabulary in addition to common words that weren't at first present. The pre-training was done with a max sentence length of 64 only for 1 epoch. **AraBERT** is an Arabic pretrained language model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup
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View details ↗bert-base-arabertv2
aubmindlab**AraBERT** is an Arabic pretrained lanaguage model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the Farasa Segmenter. We evalaute AraBERT models on different downstream tasks and compare them to mBERT), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD
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View details ↗bert-large-arabertv02
aubmindlab**AraBERT** is an Arabic pretrained lanaguage model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the Farasa Segmenter. We evalaute AraBERT models on different downstream tasks and compare them to mBERT), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD
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View details ↗bert-large-arabertv02-twitter
aubmindlab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-large-arabertv2
aubmindlab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Audar-ASR-V1-Flash
audarai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Audar-ASR-V1-Turbo
audarai🧭 Overview · 📊 Benchmarks · 💻 GGUF Deploy · 🤗 Transformers · 🎙️ Streaming · ⚡ vLLM · 📄 Tech Report · 🐙 GitHub · ☁️ Audar API · 📜 License **Audar-ASR-V1-Turbo** is an **Arabic-first generative speech-recognition model** — the accuracy tier of the Audar-ASR family. It recasts transcription as **audio-conditioned next-token prediction** over a unified text vocabulary (a language-model decoder rather than a CTC or transducer objective), and is built on a permissively-licensed open-weight audio-LLM foundation and adapted in-house — the contribution is the adaptation (the data curriculum and the alignment rubric), not the foundation: The result is **state-of-the-art dialectal Arabic ASR** — the lowest average WER *and* CER of any evaluated system on the *Open Universal Arabic ASR Leaderboard*. It transcribes MSA and every major Arabic dialect, code-switched Arabic–English, and English, across **30 languages** in total.
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View details ↗Audar-TTS-V1-Flash
audarai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Audar-TTS-V1-Turbo
audaraiAudar-TTS-V1-Turbo is the **balanced production tier** of the Audar-TTS family — a **~1.64B-parameter** community-licensed speech model that trades a little of Flash's raw speed for higher fidelity and steadier prosody, while staying efficient. It is **Arabic-first** (including Gulf/Emirati and other dialects), fully **bilingual with English**, and **clones any voice from a 5–15 second reference clip** with no per-speaker fine-tuning. This repository ships it as **GGUF quantizations** for `llama.cpp`. Turbo treats speech synthesis as **next-token prediction**: a language-model backbone predicts discrete **audar-codec** acoustic tokens, which a lightweight neural codec decodes to **24 kHz** audio. There is **no phonemizer and no per-language G2P** — so Arabic dialects and Arabic⇄English code-switching are handled from data rather than brittle pronunciation rules. > **GGUF weights** under the **AudarAI Community License v1.0** — free for research and limited > commercial use by smaller organizations; larger/enterprise use needs an AudarAI Enterprise > License. See License.
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View details ↗Please note that this model is for research purpose only. A commercial license for a model that has been trained on much more data can be acquired with audEERING. The model expects a raw audio signal as input, and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on MSP-Podcast (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An ONNX export of the model is available from doi:10.5281/zenodo.6221127. Further details are given in the associated paper and tutorial.
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View details ↗The model expects a raw audio signal as input and outputs predictions for age in a range of approximately 0...1 (0...100 years) and gender expressing the probababilty for being child, female, or male. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on aGender, Mozilla Common Voice, Timit and Voxceleb 2. For this version of the model we trained all 24 transformer layers. An ONNX export of the model is available from doi:10.5281/zenodo.7761387. Further details are given in the associated paper and tutorial.
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View details ↗The model expects a raw audio signal as input and outputs predictions for age in a range of approximately 0...1 (0...100 years) and gender expressing the probababilty for being child, female, or male. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on aGender, Mozilla Common Voice, Timit and Voxceleb 2. For this version of the model we only trained the first six transformer layers. An ONNX export of the model is available from doi:10.5281/zenodo.7761387. Further details are given in the associated paper and tutorial.
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View details ↗audio.cpp-gguf
audio-cppThis directory contains audio.cpp-native GGUF conversions of multiple speech models. These files are intended for use with audio.cpp. For conversion details, supported layouts, direct-file loading, sidecar embedding, and the latest compatibility notes, see the audio.cpp GGUF guide: The table lists the GGUF packages currently provided by this repository. License names follow audio.cpp's model license reference, which also records commercial-use conditions and other important notes.
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View details ↗Audio-Flamingo-GGUF
audio-cppThese self-contained GGUFs package the model tensors, tokenizer, configuration, and audio.cpp model spec for Audio Flamingo 3, Audio Flamingo Next, and Music Flamingo. AF3 and Next support transcription, captioning, and questions about speech, music, and environmental sounds. Music Flamingo specializes in music understanding; it produces text, not audio. Select a GGUF with `--model`. Use a question such as `Describe the instruments and tempo.` for general audio understanding. Requested timestamps or speaker descriptions are generated text, not structured alignment or diarization results. For server use, configure family `audio_flamingo`, task `asr`, and mode `offline`, then submit audio to `/v1/audio/transcriptions` with the instruction in `prompt`.
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