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AI モデル 85003

• KhanhTTS-OmniVoice↗

  kjanh

• polyglot-lion-1.7b↗

  knoveleng

  **Polyglot-Lion-1.7B: High-accuracy multilingual ASR for Singapore — English, Mandarin, Tamil & Malay** Polyglot-Lion-1.7B was developed by Quy-Anh Dang and Chris Ngo at Knovel Engineering and presented in the report "Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR". The model is obtained by fine-tuning Qwen3-ASR-1.7B exclusively on publicly available speech corpora covering Singapore's four official languages. It utilizes a balanced sampling strategy that equalizes the number of training utterances per language and deliberately omits language-tag conditioning, allowing the model to learn to identify languages implicitly from audio.

• GLM-5.3-Flash-Mixed-Q2-Q4-GGUF↗

  kyuz0

• CodeLlama-7B-KStack-GGUF↗

  lmstudio-community

• MiniMax-M2.5-GGUF↗

  lmstudio-community

• Qwen3-1.7B-MLX-8bit↗

  lmstudio-community

  This model lmstudio-community/Qwen3-1.7B-MLX-8bit was converted to MLX format from Qwen/Qwen3-1.7B using mlx-lm version **0.24.0**.

• hyenadna-large-1m-seqlen-hf↗

  LongSafari

  Welcome! HyenaDNA is a long-range genomic foundation model pretrained on context lengths of up to **1 million tokens** at **single nucleotide resolution**. You'll see models of different sizes and sequence lengths. There are also original weights-only versions of each model in the LongSafari organization, which are designed to be loaded with the original github repo. These models have identical outputs to the models in the collection above, just different interfaces. In this brief code sample we demonstrate fine-tuning HyenaDNA on a sequence classification task. This sample uses the `medium` checkpoint, with a maximum sequence length of 160k nucleotides. Note that training will fail if you use a sequence length longer than the maximum supported length for your chosen checkpoint.

• POVMissionLTX25↗

  lynaNSFW

• test-simple-tagger-tiny↗

  lysandre

• xlm-roberta-longformer-base-4096↗

  markussagen

  XLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer pre-training scheme on the English WikiText-103 corpus. The reason for this was to investigate methods for creating efficient Transformers for low-resource languages, such as Swedish, without the need to pre-train them on long-context datasets in each respecitve language. The trained model came as a result of a master thesis project at Peltarion and was fine-tuned on multilingual quesion-answering tasks, with code available here. Since both XLM-R model and Longformer models are large models, it it recommended to run the models with NVIDIA Apex (16bit precision), large GPU and several gradient accumulation steps.