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AI Models 84759

• G2-9B-Blackout-R1-GGUF↗

  bartowski

• mistralai_Voxtral-Small-24B-2507-GGUF↗

  bartowski

  Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (mistralai_Voxtral-Small-24B-2507-Q8_0) or download them all in place (./)

• pszemraj_jamba-900M-v0.13-KIx2-GGUF↗

  bartowski

• smoothMixWan22-I2V-GGUF↗

  Bedovyy

• Qwen3.8-27B-Uncensored-GGUF↗

  bgdfyymbw

• SecureBERT2.0-biencoder↗

  cisco-ai

  The **SecureBERT 2.0 Bi-Encoder** is a cybersecurity-domain sentence-similarity and document-embedding model fine-tuned from SecureBERT 2.0. It independently encodes queries and documents into a shared vector space for **semantic search**, **information retrieval**, and **cybersecurity knowledge retrieval**. The Bi-Encoder encodes queries and documents **independently** into a joint vector space. This architecture enables scalable **approximate nearest-neighbor search** for candidate retrieval and semantic ranking. The model was optimized to maximize semantic similarity between relevant cybersecurity text pairs using contrastive learning.

• distilroberta-base-climate-tcfd↗

  climatebert

• parakeet-rnnt-0.6b-GGUF↗

  cstr

• vits-cantonese-hf-xiaomaiiwn↗

  csukuangfj

• optical-flow-perceiver↗

  deepmind