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AI 모델 84760

• GLM-5.3-Flash-BF16↗

  Sciguy429

• LatentUM-Decoder↗

  SJTU-DENG-Lab

• Qwen3.5-9B-DeepSeek-V4-Flash-AWQ-W4A16↗

  Sohailhosseini

• Qwen3.8-27B-TURBO-Fable-Cold-Fusion-NEO-CODER-NVFP4↗

  Solstice-AI

• stable-audio-open-1.0↗

  Stability AI

• ecg-ptbxl-classification↗

  Steenslid

  Multi-label classification of 5 cardiovascular superclasses (NORM, MI, STTC, CD, HYP) from 12-lead ECG recordings, trained on PTB-XL. - `ecg_model.keras` | trained model - `normalisation_params.npz` | per-channel mean and std (z-score, from training fold) - `thresholds.json` | per-class decision thresholds optimised on the validation fold

• wav2vec2-base-superb-ks↗

  superb

  The base model is wav2vec2-base, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used Speech Commands dataset v1.0 for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. For the original model's training and evaluation instructions refer to the S3PRL downstream task README.

• faster-whisper-medium↗

  Systran

  This repository contains the conversion of openai/whisper-medium to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the `compute_type` option in CTranslate2.

• Llama-2-Coder-7B-GGUF↗

  TheBloke

  GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `apache-2.0`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

• merlyn-education-corpus-qa-v2-GGUF↗

  TheBloke