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AI 模型 84760
- reynaerde-7b-simpo-GGUF↗
mradermacher
- SERA-32B-i1-GGUF↗
mradermacher
- Spiral-DeepSeek-R1-Distill-Qwen-7B-i1-GGUF↗
mradermacher
- T3Q-ko-solar-sft-v2.0-i1-GGUF↗
mradermacher
- TableLLM-7b-GGUF↗
mradermacher
- WeirdCompound-v1.6-24b-i1-GGUF↗
mradermacher
- WukongV2-Mixtral-8x7B-V0.1-GGUF↗
mradermacher
- gemma-3n-E4B-it-absolute-heresy-MPOA-imatrix-GGUF↗
MuXodious
- mistralai_Ministral-3-3B-Instruct-2512-BF16-abliterated↗
Nitral-Archive
- llama-nemotron-embed-vl-1b-v2↗
NVIDIA
llama-nemotron-embed-vl-1b-v2 was developed by NVIDIA for **multimodal** question-answering retrieval. The model can embed document pages in the form of image, text, or combined image–text inputs. Documents can be retrieved given a user query in text form. The model supports page images containing text, tables, charts, and infographics. We report the evaluation of this model on two internal multimodal retrieval benchmarks, and on the popular ViDoRe V1 and V2 benchmarks and the new Vidore V3 benchmark. An embedding model is a crucial component of a retrieval system because it transforms information into dense vector representations. An embedding model is typically a transformer encoder that processes tokens of input text or images (for example, questions, passages, or page images) to output an embedding. llama-nemotron-embed-vl-1b-v2 is a combined language and vision model. The llama-nemotron-embed-vl-1b-v2 is part of the Nemotron RAG collection of open models available on HuggingFace. It is also available for optimized inference as a NIM (NVIDIA Inference Microservice) from NVIDIA NeMo Retriever, which provides state-of-the-art, commercially-ready models and microservices optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can readily customize them for domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.