CASA-Qwen2_5-VL-3B
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helium-1-2b
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View details ↗helium-1-preview-2b
kyutaiHelium-1 preview is a lightweight language model with 2B parameters, targeting edge and mobile devices. It supports the following languages: English, French, German, Italian, Portuguese, Spanish. ⚠️ Helium-1 Preview is a base model, which was not fine-tuned to follow instructions or human preferences. For most downstream use cases, the model should be aligned with supervised fine-tuning, RLHF or related methods. The intended use of the Helium model is research and development of natural language processing systems, including but not limited to language generation and understanding. The model can be used in English, French, German, Italian, Portuguese and Spanish. For most downstream use cases, the model should be aligned with supervised fine-tuning, RLHF or related methods.
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View details ↗hibiki-1b-pytorch-bf16
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View details ↗hibiki-2b-pytorch-bf16
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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 ↗mimi
kyutaiMimi codec is a state-of-the-art audio neural codec, developped by Kyutai, that combines semantic and acoustic information into audio tokens running at 12.5Hz and a bitrate of 1.1kbps. Mimi is a high-fidelity audio codec leveraging neural networks. It introduces a streaming encoder-decoder architecture with quantized latent space, trained in an end-to-end fashion. It was trained on speech data, which makes it particularly adapted to train speech language models or text-to-speech systems.
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View details ↗moshika-candle-q8
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View details ↗moshika-mlx-q4
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View details ↗moshika-mlx-q8
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View details ↗moshika-pytorch-bf16
kyutaiMoshi is a speech-text foundation model that casts spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. Moshi also predicts time-aligned text tokens as a prefix to audio tokens. This “Inner Monologue” method significantly improves the linguistic quality of generated speech and provides streaming speech recognition and text-to-speech. As a result, Moshi is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice. The model can be used as a conversational agent for casual conversations, basic facts and advice (e.g. recipes, trivia), roleplay, etc. However, the model has limited abilities for complex tasks and cannot access tools, but rather focues on natural, low-latency interactions. Some components of the model can be used independently or repurposed relatively easily. For instance the Mimi codec is a state-of-the-art audio neural codec that combines semantic and acoustic information into audio tokens running at 12Hz and a bitrate of 1.1kbps, which make it particularly adapted to train speech language models or text-to-speech systems.. Regarding the main Moshi architecture, other downstream usecases would require some finetuning / domain adaptation.
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View details ↗moshika-pytorch-q8
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View details ↗moshika-rag-pytorch-bf16
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View details ↗moshiko-candle-q8
kyutaiMoshi is a speech-text foundation model that casts spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. Moshi also predicts time-aligned text tokens as a prefix to audio tokens. This “Inner Monologue” method significantly improves the linguistic quality of generated speech and provides streaming speech recognition and text-to-speech. As a result, Moshi is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice. The model can be used as a conversational agent for casual conversations, basic facts and advice (e.g. recipes, trivia), roleplay, etc. However, the model has limited abilities for complex tasks and cannot access tools, but rather focues on natural, low-latency interactions. Some components of the model can be used independently or repurposed relatively easily. For instance the Mimi codec is a state-of-the-art audio neural codec that combines semantic and acoustic information into audio tokens running at 12Hz and a bitrate of 1.1kbps, which make it particularly adapted to train speech language models or text-to-speech systems.. Regarding the main Moshi architecture, other downstream usecases would require some finetuning / domain adaptation.
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View details ↗moshiko-mlx-q4
kyutaiMoshi is a speech-text foundation model that casts spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. Moshi also predicts time-aligned text tokens as a prefix to audio tokens. This “Inner Monologue” method significantly improves the linguistic quality of generated speech and provides streaming speech recognition and text-to-speech. As a result, Moshi is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice. The model can be used as a conversational agent for casual conversations, basic facts and advice (e.g. recipes, trivia), roleplay, etc. However, the model has limited abilities for complex tasks and cannot access tools, but rather focues on natural, low-latency interactions. Some components of the model can be used independently or repurposed relatively easily. For instance the Mimi codec is a state-of-the-art audio neural codec that combines semantic and acoustic information into audio tokens running at 12Hz and a bitrate of 1.1kbps, which make it particularly adapted to train speech language models or text-to-speech systems.. Regarding the main Moshi architecture, other downstream usecases would require some finetuning / domain adaptation.
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View details ↗moshiko-mlx-q8
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View details ↗moshiko-pytorch-bf16
kyutaiMoshi is a speech-text foundation model and full-duplex spoken dialogue framework Moshi is a speech-text foundation model that casts spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. Moshi also predicts time-aligned text tokens as a prefix to audio tokens. This “Inner Monologue” method significantly improves the linguistic quality of generated speech and provides streaming speech recognition and text-to-speech. As a result, Moshi is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice. - **Developed by:** Kyutai - **Model type:** Multimodal speech-text foundation model - **Language(s) (NLP):** English - **License:** CC-BY
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View details ↗moshiko-pytorch-q8
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View details ↗personaplex-rl-seamless
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View details ↗pocket-tts
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View details ↗Supports Python 3.10, 3.11, 3.12, 3.13 and 3.14. Requires PyTorch 2.5+. Does not require the gpu version of PyTorch. 🔊 Demo | 🐱💻GitHub Repository | 🤗 Hugging Face Model Card | ⚙️ Tech report | 📄 Paper | 📚 Documentation > [!NOTE] > **New (August 2026):** We've released the training code! Check out `training/` to start training your own models. > Open a PR to add your model to the Models trained by the community section.
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View details ↗Sequential_Helium_6B
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View details ↗stt-1b-en_fr-trfs
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View details ↗stt-2.6b-en-trfs
kyutaiThis is a model for streaming speech-to-text (STT, also known as automatic speech recognition, ASR). Unlike offline speech-to-text, where the model needs the entire audio to produce the transcript, our model starts to output the transcript as soon as a few seconds of audio become available. The model architecture is a Transformer that consumes audio tokenized by Mimi (see the Moshi paper) and outputs text tokens. The frame rate is 12.5 Hz and each audio frame is represented by 32 audio tokens. We release two models: - `kyutai/stt-1b-en_fr`, an English and French model with ~1B parameters, a 0.5 second delay, and a semantic VAD. - `kyutai/stt-2.6b-en`, an English-only model with ~2.6B parameters and a 2.5 second delay.
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