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AI 모델 84759
- UnifiedReward-Think-qwen3vl-8b-i1-GGUF↗
mradermacher
- VLAA-Thinker-Qwen2.5VL-7B-GGUF↗
mradermacher
- Zira-Z.1-i1-GGUF↗
mradermacher
- SmolVLM-256M-Instruct-GGUF↗
Mungert
- SmolLM3-3B-flax↗
notpaulmartin
- Hymba-1.5B-Base↗
NVIDIA
- llama-nemotron-rerank-1b-v2↗
NVIDIA
The Llama Nemotron Reranking 1B model is optimized for providing a logit score that represents how relevant a document(s) is to a given query. The model was fine-tuned for **multilingual, cross-lingual** text question-answering retrieval, with support for **long documents (up to 8192 tokens)**. This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish. This model is a component in a text retrieval system to improve the overall accuracy. A text retrieval system often uses an embedding model (dense) or lexical search (sparse) index to return relevant text passages given the input. A reranking model can be used to rerank the potential candidate into a final order. The reranking model has the question-passage pairs as an input and therefore, can process cross attention between the words. It’s not feasible to apply a Ranking model on all documents in the knowledge base, therefore, ranking models are often deployed in combination with embedding models. The Llama Nemotron Reranking 1B model is a part of the NeMo Retriever collection of NIM, which provide 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 also readily customize them for their domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.
- GreatCaptainNemo-ProLLaMA-GGUF-smashed↗
PrunaAI
- calme-2.3-phi3-4b-GGUF↗
QuantFactory
- Oumuamua-7b-instruct-v2-GGUF↗
QuantFactory