KasbahTTS-V0
MenaVoice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 ↗DISCOVER & COMPARE
Explore AI models 85003
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View details ↗This model is a fine-tuned version of menhior/wav2vec2-large-xls-r-300m-turkish-colab-full on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5709 - Wer: 0.2893 The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40
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View details ↗Spark-X2.5-1.7B
MENIARAYOUB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 ↗Jan-nano
Menlo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 ↗Jan-nano-128k
Menlo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 ↗Jan-nano-128k-gguf
Menlo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 ↗Jan-nano-gguf
MenloJan Nano is a fine-tuned language model built on top of the Qwen3 architecture. Developed as part of the Jan ecosystem, it balances compact size and extended context length, making it ideal for efficient, high-quality text generation in local or embedded environments.
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View details ↗Lucy-128k-gguf
Menlo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 ↗Lucy-gguf
Menlo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 ↗Qwen3.8-Flash-Next-REAP-384-mlx-mtp
mensaprodigy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 ↗mental-bert-base-uncased
mental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 ↗mental-roberta-base
mental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 ↗Dolphin3-Cyber-8B-GGUF
meowmeow123ok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 ↗Qwen3.5-9B-GGUF
Mer0vin8ian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 ↗mera-mix-4x7B
meraGPTThis is a mixture of experts (MoE) model that is half as large (4 experts instead of 8) as the Mixtral-8x7B while been comparable to it across different benchmarks. You can use it as a drop in replacement for your Mixtral-8x7B and get much faster inference. mera-mix-4x7B achieves the score of 75.91 on the OpenLLM Eval and compares well with 72.7 by Mixtral-8x7B and 74.46 by Mixtral-8x22B. In addition, to the official Open LLM Leaderboard, the results on OpenLLM Eval have been validated by others as well (76.59).
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View details ↗MERaLiON-2-10B-ASR
MERaLiON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 ↗MERaLiON-2-10B-GGUF
MERaLiON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 ↗MERaLiON-2-3B-GGUF
MERaLiON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 ↗MERaLiON-3-10B
MERaLiON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 ↗MERaLiON-3-3B-ASR
MERaLiON**MERaLiON-3-ASR** is the speech-recognition line in the MERaLiON-3 generation of Speech-Text Large Language Models, developed by I2R, A\*STAR, Singapore. It is purpose-built for Singapore- and Southeast-Asia-centric ASR, with broad coverage across regional languages, dialects, and natural conversational code-switching. - **Languages:** English (Global and Singapore), Mandarin, Malay, Tamil, Indonesian, Thai, Vietnamese - **Chinese dialects:** Cantonese, Hokkien - **Code-switching:** natural conversational English ↔ {Mandarin, Malay, Tamil, Vietnamese, Cantonese, Hokkien} mixtures, including Singlish **MERaLiON-3-ASR-API** is the hosted production endpoint, tuned for real-time and enterprise transcription workloads.
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View details ↗MERaLiON-OmniVoice-Hokkien-TTS
MERaLiON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 ↗MERaLiON-SER-v1
MERaLiONYou can experience **speech emotion recognition** by selecting Gender/Speech recognition tab via our interactive Hugging Face Space: Upload an audio clip or record your voice to visualize categorical emotions and dimensional affect trajectories in real time. **MERaLiON-SER-v1** is a multilingual **speech emotion recognition (SER)** model jointly predicting 1. **Categorical emotions** – 7 discrete classes *(Neutral, Happy, Sad, Angry, Surprised, Fearful, Disgusted)*, and 2. **Dimensional affect values** – continuous *(Valence, Arousal, Dominance)*. Valence (0 negative, 1 positive), Arousal (0 calm, 1 active), Dominance (0 weak, 1 strong)
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