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AI 모델 84759
- amd.Instella-MoE-16B-A3B-Think-GGUF↗
DevQuasar
- Megamind-v2-VL-med-gguf↗
digitranslab
- piper-liana-romanian↗
eduardem
- polka-1.1b-chat↗
eryk-mazus
- GPT-OSS-Coder-GGUF↗
exalandru
A **gpt-oss-120b** version focused on improving practical coding-agent behavior in repository-level software engineering tasks. > This release adds three focused passes over re-curated data, each targeting a behaviour measured missing in the previous one: writing failure-injection regression tests before closing, iterating on multi-defect repositories past the first green suite, and honouring per-harness completion contracts. Every pass was gated by calibrated probes and a held-out benchmark ladder before being promoted. It digs deeper into the repository, follows evidence to the root cause, and keeps iterating until the fix holds under real tests instead of stopping at a plausible-looking patch.
- MobileLLM-R1.5-360M↗
facebook
- wav2vec2-large-960h-lv60-self↗
facebook
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with Self-Training objective. When using the model make sure that your speech input is also sampled at 16Khz. We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
- xmod-base-60-265k↗
facebook
- xlm-roberta-large-finetuned-conll03-german↗
FacebookAI
1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training 5. Evaluation 6. Environmental Impact 7. Technical Specifications 8. Citation 9. Model Card Authors 10. How To Get Started With the Model The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is XLM-RoBERTa-large fine-tuned with the conll2003 dataset in German. The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text.
- eng-latn-100mb-after-ppt-mp-struct-bcc-100mb-ckpt500_seed455↗
francesca9805