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AI 모델 2119

• Nemotron-3.5-Embed-8B-BF16↗

  NVIDIA

  **Nemotron-3.5-Embed-8B-BF16** is a state-of-the-art, versatile embedding model developed by NVIDIA for multimodal and multilingual question-answering retrieval. The model can embed a document page in the form of an image, text, or combined image–text input. Documents can be retrieved given a user query in text form. The model supports page images containing text, tables, charts, and infographics. For text retrieval, the model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, and Vietnamese. For image retrieval, it was evaluated across 18 languages: Arabic, Bengali, Chinese, English, Persian, Finnish, French, German, Hindi, Indonesian, Japanese, Korean, Russian, Spanish, Swahili, Telugu, Thai, and Yoruba. The model may support additional languages beyond these evaluation sets. Nemotron-3.5-Embed-8B-BF16 achieves state-of-the-art results across multiple multilingual and multimodal retrieval benchmarks. Read more details in our Blog Post. The **Nemotron-3.5-Embed-8B-BF16** is suitable for users who want to build a multimodal question-and-answer application over a large corpus, leveraging the latest dense retrieval technology. The input of the model is a text or document image and the output is a fixed-size embedding vector. The embedding model is a bi-encoder that supports context in textual format (e.g. the query or the OCR text of a page or a section of a document) or the image of a document page. Typically, the embedding model is used first to embed (vectorize) the whole corpus (document images or text chunks), and embeddings are stored in a vector database associated to its raw content (image or text). Then at inference time, the embedding model is used to embed the query. The model generates dense vector embeddings from text or image inputs, enabling multimodal retrieval, semantic search, and (agentic) RAG workflows.

• Nemotron-3-Embed-1B-BF16↗

  NVIDIA

  **Nemotron-3-Embed-1B-BF16** is a versatile text embedding model trained by NVIDIA and optimized for retrieval and semantic similarity tasks. It provides strong multilingual and cross-lingual retrieval capabilities and is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, Vietnamese. The model generates dense vector embeddings from multilingual text inputs, enabling retrieval, semantic search, and (agentic) RAG workflows. As a core component of text retrieval systems, an embedding model transforms text, such as questions or passages, into dense vector representations. These models are typically transformer encoders that process input tokens and produce embeddings suitable for efficient similarity matching. Among models of comparable size, **Nemotron-3-Embed-1B-BF16** achieves state-of-the-art performance across multiple multilingual retrieval benchmarks. Read more details in our Blog Post.

• Srijith-rkr_-_deepseek_base_1e-4_NO_cot_only_failed_samples_100_ckpt-gguf↗

  RichardErkhov

• mask2former-swin-small-coco-instance↗

  facebook

  Mask2Former model trained on COCO instance segmentation (small-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.

• dab-detr-resnet-50↗

  IDEA-Research

  1. Model Details 2. Model Sources 3. How to Get Started with the Model 4. Training Details 5. Evaluation 6. Model Architecture and Objective 7. Citation > We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer-by-layer. Using box coordinates not only helps using explicit positional priors to improve the query-to-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer-by-layer in a cascade manner. As a result, it leads to the best performance on MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7\% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods. This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Muse-Glimmer-30B-GGUF↗

  NANI-Nithin

  GGUF builds of meta-models/Muse-Glimmer-30B — Meta Superintelligence Lab's ~30B multimodal agent model, distilled from Muse Spark and purpose-built for autonomous agentic work on consumer hardware. Every file here was cut from the same BF16 source weights; the repo holds full-precision copies, a vision projector, and 29 quants from Q8_0 down to Q1_0. Quantized 2026-09-07. **These are llama.cpp GGUF files.** They are not loaded by vLLM, SGLang or `transformers` the way the source repo is. Muse Glimmer is a 30B-class dense causal transformer with a dedicated perception encoder (image + text input, text output), trained for the capabilities an autonomous agent needs working together:

• gemma4-coding-agent↗

  S4MPL3BI4S

  **Example usage**: - For text only LLMs: `llama-cli -hf S4MPL3BI4S/gemma4-coding-agent --jinja` - For multimodal models: `llama-mtmd-cli -hf S4MPL3BI4S/gemma4-coding-agent --jinja` To create an Ollama model from this vision model: 1. Place the `Modelfile` in the same directory as the finetuned bf16 merged model 3. Run: `ollama create model_name -f ./Modelfile` (Replace `model_name` with your desired name)

• Phi-3-medium-128k-instruct↗

  Microsoft

  🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) The Phi-3-Medium-128K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Medium version in two variants 4k and 128K which is the context length (in tokens) that it can support. The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3-Medium-128K-Instruct showcased a robust and state-of-the-art performance among models of the same-size and next-size-up.

• eslav_PP-OCRv5_mobile_rec↗

  PaddlePaddle

  eslav_PP-OCRv5_mobile_rec is one of the PP-OCRv5_rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of East Slavic languages. The key accuracy metrics are as follow: **Note**: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications. You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine.

• samantha-hands-gguf↗

  trommatic