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AI モデル 84759
- moonshotai.Kimi-Dev-72B-GGUF↗
DevQuasar
- distil-small.en↗
distil-whisper
Distil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. It is a distilled version of the Whisper model that is **6 times faster**, 49% smaller, and performs **within 1% WER** on out-of-distribution evaluation sets. This is the repository for distil-small.en, a distilled variant of Whisper small.en. It is the **smallest Distil-Whisper checkpoint**, with just 166M parameters, making it the ideal choice for memory constrained applications (e.g. on-device). For most other applications, the distil-medium.en or distil-large-v2 checkpoints are recommended, since they are both faster and achieve better WER results:
- qwen3vl-8b-invoice-fp8-r2↗
Docunetix
- deepseek-v3-random↗
eagle618
- Ternary-Bonsai-2-27B-MLX-2bit↗
EigenLabs
- dragon-roberta-context-encoder↗
facebook
- hiera_small_224.mae_in1k↗
facebook
- mms-tts-jmd↗
facebook
- xglm-564M↗
facebook
XGLM-564M is a multilingual autoregressive language model (with 564 million parameters) trained on a balanced corpus of a diverse set of 30 languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\* (\*Equal Contribution). The original implementation was released in this repository. For intended usage of the model, please refer to the model card released by the XGLM-564M development team. The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi.
- cognitivecomputations-Llama-3-8B-Instruct-abliterated-v2-GGUF↗
featherless-ai-quants