Block-wise FP8 quantization of Qwen/Qwen3-Omni-30B-A3B-Instruct. **Quantized to FP8:** - Thinker (48 layers MoE) - main language model - Talker (20 layers MoE) - audio generation model **Kept in BF16:** - Vision encoder (`thinker.visual`) - Audio tower (`thinker.audio_tower`) - Code2Wav decoder (`code2wav`) - Embedding layers - LayerNorm layers - MoE gate routing layers
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View details ↗Standalone ECAPA-TDNN voice encoder extracted from Qwen/Qwen3-TTS-12Hz-1.7B-Base. Produces **2048-dimensional** x-vector speaker embeddings from audio. The encoder follows the ECAPA-TDNN architecture (Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification) and uses Res2Net blocks, squeeze-excitation attention, and attentive statistical pooling. > [!WARNING] > **Only extracted from the `*-Base` model variant.** The `*-CustomVoice` and `*-VoiceDesign` variants do **NOT** support speaker embeddings and will not work. Do not attempt to use them for speaker encoding.
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View details ↗XLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer pre-training scheme on the English WikiText-103 corpus. The reason for this was to investigate methods for creating efficient Transformers for low-resource languages, such as Swedish, without the need to pre-train them on long-context datasets in each respecitve language. The trained model came as a result of a master thesis project at Peltarion and was fine-tuned on multilingual quesion-answering tasks, with code available here. Since both XLM-R model and Longformer models are large models, it it recommended to run the models with NVIDIA Apex (16bit precision), large GPU and several gradient accumulation steps.
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View details ↗In this work, we introduce two state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. The benchmarking results show that the Marqo-Ecommerce models consistently outperformed *all other models* across various metrics. Specifically, `marqo-ecommerce-L` achieved an average improvement of **17.6% in MRR** and **20.5% in nDCG@10** when compared with the current best open source model, `ViT-SO400M-14-SigLIP` across all three tasks in the `marqo-ecommerce-hard` dataset. When compared with the best private model, `Amazon-Titan-Multimodal`, we saw an average improvement of **38.9% in MRR** and **45.1% in nDCG@10** across all three tasks, and **35.9% in Recall** across the Text-to-Image tasks in the `marqo-ecommerce-hard` dataset. **Released Content**: 1) Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models 2) GoogleShopping-1m and AmazonProducts-3m for evaluation 3) Evaluation Code
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View details ↗In this work, we introduce two state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. The benchmarking results show that the Marqo-Ecommerce models consistently outperformed *all other models* across various metrics. Specifically, `marqo-ecommerce-L` achieved an average improvement of **17.6% in MRR** and **20.5% in nDCG@10** when compared with the current best open source model, `ViT-SO400M-14-SigLIP` across all three tasks in the `marqo-ecommerce-hard` dataset. When compared with the best private model, `Amazon-Titan-Multimodal`, we saw an average improvement of **38.9% in MRR** and **45.1% in nDCG@10** across all three tasks, and **35.9% in Recall** across the Text-to-Image tasks in the `marqo-ecommerce-hard` dataset. **Released Content**: 1) Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models 2) GoogleShopping-1m and AmazonProducts-3m for evaluation 3) Evaluation Code
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View details ↗Marqo-FashionCLIP and Marqo-FashionSigLIP outperform the previous state-of-the-art fashion CLIP models (see results below). Marqo-FashionCLIP leverages Generalised Contrastive Learning (GCL) which allows the model to be trained on not just text descriptions but also categories, style, colors, materials, keywords and fine-details to provide highly relevant search results on fashion products. The model was fine-tuned from ViT-B-16 (laion2b_s34b_b88k). **Github Page**: Marqo-FashionCLIP The model can be seamlessly used with OpenCLIP by
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View details ↗**Marqo Fashion Siglip 2 is available.** marqo-fashion-SigLip-2 has shown a further 78% improvement in MMR and recall vs marqo-fashion-SigLip. Contact Marqo to learn more: https://www.marqo.ai/book-demo Marqo-FashionSigLIP is a multimodal embedding model that provides up to 57% improvement in MRR and recall over fashion clip. Marqo-FashionSigLIP leverages Generalised Contrastive Learning (GCL) which allows the model to be trained on not just text descriptions but also categories, style, colors, materials, keywords and fine-details to provide highly relevant search results on fashion products. The model was fine-tuned from ViT-B-16-SigLIP (webli).
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View details ↗__NOTE: Like all models, this one can make mistakes. NSFW content can be subjective and contextual, this model is intended to help identify this content, use at your own risk.__ `Marqo/nsfw-image-detection-384` is a lightweight image classification model designed to identify NSFW images. The model is approximately 18–20x smaller than other open-source models and achieves a superior accuracy of 98.56% on our dataset. This model uses 384x384 pixel images for the input with 16x16 pixel patches. This model was trained on a proprietary dataset of 220,000 images. The training set includes 100,000 NSFW examples and 100,000 SFW examples, while the test set contains 10,000 NSFW examples and 10,000 SFW examples. This dataset features a diverse range of content, including: real photos, drawings, Rule 34 material, memes, and AI-generated images. The definition of NSFW can vary and is sometimes contextual, our dataset was constructed to contain challenging examples however this definition may not be 100% aligned with every use case, as such we recommend experimenting and trying different thresholds to determine if this model is suitable for your needs.
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View details ↗This model is a fine-tuned version of the DistilBERT model to classify toxic comments. This model is intended to use for classify toxic online classifications. However, one limitation of the model is that it performs poorly for some comments that mention a specific identity subgroup, like Muslim. The following table shows a evaluation score for different identity group. You can learn the specific meaning of this metrics here. But basically, those metrics shows how well a model performs for a specific group. The larger the number, the better. The table above shows that the model performs poorly for the muslim and jewish group. In fact, you pass the sentence "Muslims are people who follow or practice Islam, an Abrahamic monotheistic religion." Into the model, the model will classify it as toxic. Be mindful for this type of potential bias.
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