cue-detr
disco-eth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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Llama3-German-8B
DiscoResearch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 ↗mixtral-7b-8expert
DiscoResearch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 ↗electrical-ner-ModernBERT-base
disham993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 ↗openbioner-base-v2
disi-unibo-nlp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 ↗noumenon_GGUFs
disinfozone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 ↗YuLan-Mini-GGUF-diskrot
diskrot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 ↗zeta-2.1-NVFP-GGUF
distaste447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 ↗distil-email-classifier
distil-labs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 ↗distil-home-assistant-functiongemma
distil-labs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 ↗distil-large-v2
distil-whisper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 ↗distil-large-v3
distil-whisperDistil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. This is the third and final installment of the Distil-Whisper English series. It the knowledge distilled version of OpenAI's Whisper large-v3, the latest and most performant Whisper model to date. Compared to previous Distil-Whisper models, the distillation procedure for distil-large-v3 has been adapted to give **superior long-form transcription accuracy** with OpenAI's **sequential long-form algorithm**.
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View details ↗distil-large-v3.5
distil-whisperDistil-Whisper is the knowledge-distilled version of OpenAI's Whisper-Large-v3, described in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. As the newest addition to the Distil-Whisper English family, Distil-Large-v3.5 maintains the high efficiency of its predecessors while delivering better performance. Compared to earlier models, Distil-Large-v3.5 has been trained on over 4× more diverse public data (98k hours) and uses a "patient" teacher with an extended training schedule and aggressive data augmentation (SpecAugment) during distillation. This results in enhanced robustness and accuracy compared to previous Distil-Whisper models, making it suitable as a drop-in replacement. 1. It offers a different balance between accuracy and efficiency, remains **~1.5x faster** than Whisper-Large-v3-Turbo while performing slightly better on short-form transcription and falling ~1% behind on long-form transcription. 2. It works perfectly as a draft model for **speculative decoding** with Whisper-Large-v3. By keeping the encoder frozen during training, we need to load just two extra decoder layers and forward the encoder only once. This achieves ~2x faster inference compared to Whisper-Large-v3 while maintaining identical outputs.
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View details ↗distil-large-v3.5-ct2
distil-whisperThis repository contains the model weights for distil-large-v3.5 converted to CTranslate2 format. CTranslate2 is a fast inference engine for Transformer models and is the supported backend for the Faster-Whisper package. To use the model in Faster-Whisper, first install the PyPi package according to the official instructions. For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub:
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View details ↗distil-medium.en
distil-whisperDistil-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-medium.en, a distilled variant of Whisper medium.en. **Note:** Distil-Whisper is currently only available for English speech recognition. We are working with the community to distill Whisper on other languages. If you are interested in distilling Whisper in your language, check out the provided training code. We will update the Distil-Whisper repository with multilingual checkpoints when ready!
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View details ↗distil-small.en
distil-whisperDistil-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:
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View details ↗tiny-random-mistral
distilabel-internal-testing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 ↗distilbert-base-cased
distilbertThis model is a distilled version of the BERT base model. It was introduced in this paper. The code for the distillation process can be found here. This model is cased: it does make a difference between english and English. All the training details on the pre-training, the uses, limitations and potential biases (included below) are the same as for DistilBERT-base-uncased. We highly encourage to check it if you want to know more. DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives:
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View details ↗distilbert-base-cased-distilled-squad
distilbert- Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** The DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, adistilled version of BERT, and the paper DistilBERT, adistilled version of BERT: smaller, faster, cheaper and lighter. DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than *bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark. This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.
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View details ↗distilbert-base-german-cased
distilbert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 ↗distilbert-base-multilingual-cased
distilbert1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. How To Get Started With the Model This model is a distilled version of the BERT base multilingual model. The code for the distillation process can be found here. This model is cased: it does make a difference between english and English. The model is trained on the concatenation of Wikipedia in 104 different languages listed here. The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average, this model, referred to as DistilmBERT, is twice as fast as mBERT-base.
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View details ↗distilbert-base-uncased
distilbertThis model is a distilled version of the BERT base model. It was introduced in this paper. The code for the distillation process can be found here. This model is uncased: it does not make a difference between english and English. DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives: This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks.
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View details ↗distilbert-base-uncased-distilled-squad
distilbert- Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** The DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, adistilled version of BERT, and the paper DistilBERT, adistilled version of BERT: smaller, faster, cheaper and lighter. DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than *bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark. This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.
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View details ↗- Model Details - How to Get Started With the Model - Uses - Risks, Limitations and Biases - Training **Model Description:** This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7). - **Developed by:** Hugging Face - **Model Type:** Text Classification - **Language(s):** English - **License:** Apache-2.0 - **Parent Model:** For more details about DistilBERT, we encourage users to check out this model card. - **Resources for more information:** - Model Documentation - DistilBERT paper This model can be used for topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
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