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AI モデル 84759
- Big-Lemon-Cookie-11B-GGUF↗
backyardai
- AMD-Llama-135m-code-GGUF↗
bartowski
- granite-embedding-30m-english-GGUF↗
bartowski
- huizimao_gpt-oss-120b-uncensored-bf16-GGUF↗
bartowski
- internlm_Intern-S1-GGUF↗
bartowski
- higgs-tts-3-4b↗
bosonai
Higgs TTS 3 is built for voice chat: it **speaks, not just reads**. It turns model responses into expressive conversational speech across **100+ languages**, with **zero-shot voice cloning** and **inline control** over emotion, style, prosody, pauses, and sound effects. > [!TIP] > Released for research and non-commercial use under the **Boson Higgs TTS 3 Research and Non-Commercial License**. Production, hosted APIs, embedding in a product/service, or reselling the model requires a separate commercial license. Prohibited: voice cloning without consent, impersonation, fraud, election deception, biometric surveillance, or any unlawful use. > [!TIP] > **Free for digital creators — including monetized content.** Under the license's **Creator Use Grant**, creators may use Higgs TTS 3 to make and monetize podcasts, videos, and social posts for free. The one requirement is to credit *Boson AI's Higgs Audio* — either in the audio or prominently in the accompanying text (e.g., the video description or show notes). Suggested credit: *"This audio was created with Boson AI's Higgs Audio — https://www.boson.ai/higgs-audio"*. See the Creator Use section below.
- docling-layout-old↗
docling-project
`docling-layout-old` is the old Layout Model of Docling project. This model uses the RT-DETR architecture and has been trained from scratch on a variety of document datasets.
- UNA-TheBeagle-7b-v1-imatrix-GGUF↗
duyntnet
- llama-3.2-1B-lab5-6b-d40↗
Dxnn1u
- gpt-neo-2.7B↗
EleutherAI
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model. GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.