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AI モデル 84760

• c4ai-command-r-plus↗

  Cohere

• Command-nightly↗

  Cohere

• ms-marco-MiniLM-L2-v2↗

  cross-encoder

  This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this:

• Qwen3.8-27B-Uncensored-Cyber-agentic-imatrix-GGUF↗

  cyjin-yl

  GGUF quantizations of philbert440/Qwen3.8-27B-Uncensored-Cyber, calibrated on an importance matrix built from **real agentic-coding traffic** rather than generic English prose. **All model weights and behaviour are philbert440's work. This repository contributes only the quantization and the calibration methodology** — no fine-tuning, no merging, no behavioural change of any kind was performed here. Inherited from upstream, and stated plainly so nobody is surprised: this is a **de-refusal ("abliterated") model specialized for the cyber/offensive-security domain**. The vision tower and the MTP speculative-decoding head are preserved upstream, which is what makes the multimodal path and the MTP graft work at all. Read the base model's card before using it — it documents the recipe and the evaluation.

• Qwen2.5-1.5B-Instruct-abliterated↗

  D2ziggy

• Gemma-3-12b-it-MAX-HORROR-Imatrix-GGUF↗

  DavidAU

• don-thomas-llm↗

  donthomas03

• Llama-3-Instruct-8B-SPPO-Iter3-imatrix-GGUF↗

  duyntnet

• Qwen3-30B-A3B-GGUF↗

  eaddario

• qwen3-4b-deslop-v4-GGUF↗

  erikkannike