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AI 모델 85003

• webrl-glm-4-9b↗

  zai-org

• turkish-ai-text-detector-berturk-v11-gemini↗

  ads2009

• crossencoder-mMiniLMv2-L6-mmarcoFR↗

  antoinelouis

  This is a cross-encoder model for French. It performs cross-attention between a question-passage pair and outputs a relevance score. The model should be used as a reranker for semantic search: given a query and a set of potentially relevant passages retrieved by an efficient first-stage retrieval system (e.g., BM25 or a fine-tuned dense single-vector bi-encoder), encode each query-passage pair and sort the passages in a decreasing order of relevance according to the model's predicted scores. Here are some examples for using the model with Sentence-Transformers, FlagEmbedding, or Huggingface Transformers. The model is evaluated on the smaller development set of mMARCO-fr, which consists of 6,980 queries for which an ensemble of 1000 passages containing the positive(s) and ColBERTv2 hard negatives need to be reranked. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k). To see how it compares to other neural retrievers in French, check out the *DécouvrIR* leaderboard.

• NousResearch_DeepHermes-3-Llama-3-3B-Preview-GGUF↗

  bartowski

• Ornith-1.5-122B-GGUF↗

  bartowski

  **Model details:** - Parameter count: 125B (source checkpoint) - Input support: text, image (with mmproj file) - details - Speculative decoding: yes (MTP) - details - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (78.92GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

• ozone-ai_0x-lite-GGUF↗

  bartowski

• Scarlett-Llama-3-8B-GGUF↗

  bartowski

• beetle-bilingual-l2-50-simultaneous-b2-fineweb-nld-eng↗

  Beetle-FineWeb3-24B

• Param-1-5B↗

  bharatgenai

• twitter-roberta-base-offensive↗

  cardiffnlp

  This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.