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View details ↗kaggle
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View details ↗lambda
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unslothaiThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]
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View details ↗uform3-image-text-english-large
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View details ↗uform3-image-text-multilingual-base
unum-cloudPocket-Sized Multimodal AI For Content Understanding and Generation In Python, JavaScript, and Swift The `uform3-image-text-multilingual-base` UForm model is a tiny vision and multilingual language encoder, covering __21 languages__, mapping them into a shared vector space. This model produces up to __256-dimensional embeddings__ and is made of: Unlike most CLIP-like multomodal models, this model shares 4 layers between the text and visual encoder to allow for more data- and parameter-efficient training. Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code. If you need a larger, more accurate, or multilingual model, check our HuggingFace Hub. For more details on running the model, check out the UForm GitHub repository.
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View details ↗bge-m3-korean
upskyyThis model is korsts and kornli finetuning model from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. - **Model Type:** Sentence Transformer - **Base model:** BAAI/bge-m3 - **Maximum Sequence Length:** **8192 tokens** - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗mHuBERT-147
utter-project**MODEL DETAILS:** 3rd iteration, K=1000, HuBERT base architecture (95M parameters), 147 languages. mHuBERT-147 are compact and competitive multilingual HuBERT models trained on 90K hours of open-license data in 147 languages. Different from *traditional* HuBERTs, mHuBERT-147 models are trained using faiss IVF discrete speech units. Training employs a two-level language, data source up-sampling during training. See more information in our paper. 1. Summary 2. Training Data and Code 3. ML-SUPERB Scores 4. Languages and Datasets 5. Intermediate Checkpoints 6. Citing and Funding Information
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View details ↗mHuBERT-147-base-2nd-iter
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View details ↗SauerkrautLM-Reason-EuroColBERT
VAGOsolutions**Knowledge Distillation** from strong synthetic data (200k samples generated with Qwen/Qwen3-32B-AWQ and scored by a high-performing reranker). **Robust 210M parameter architecture** optimized for multilingual reasoning-focused retrieval without compression trade-offs. With **210 million parameters** – that's **less than 1/33rd the size** of some competing models – SauerkrautLM-Reason-EuroColBERT achieves or exceeds the performance of: - Models with **over 7 billion parameters** (33× larger than ours) - Proprietary API-based solutions from major tech companies - Specialized reasoning models like ReasonIR-8B (38× larger) This balanced architecture provides exceptional performance while remaining deployable on standard infrastructure.
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