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AI モデル 85003

• Gigaberg-Mistral-Large-123B-GGUF↗

  bartowski

• Gryphe_Pantheon-Proto-RP-1.8-30B-A3B-GGUF↗

  bartowski

• mamba-2.8b-hf-GGUF↗

  bartowski

• Qwen3.5-4B-GGUF↗

  Boollm

• brant-pretrained↗

  braindecode

• MegaDescriptor-DINOv2-518↗

  BVRA

  A DINOv2 based image feature model. Supervisely pre-trained on animal re-identification datasets. Original model: timm/vit_large_patch14_dinov2.lvd142m - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 304.4 - GMACs: 507.1 - Activations (M): 1058.8 - Image size: 518 x 518

• Qwen3.8-27B-Uncensored-Heretic-MTP-UD-GGUF↗

  codavidgarcia

  GGUF quants of llmfan46/Qwen3.8-27B-Uncensored-Heretic-Native-MTP-Preserved, built so the model **and its native MTP head** fit fully in VRAM on common consumer cards. Each file applies Unsloth's per-tensor "UD" recipe for the base Qwen3.8-27B, copied tensor by tensor, to llmfan46's weights. The MTP block (`blk.64`) stays at q6_K / q8_0 in every tier. Mean KL divergence against a Q8_0 reference of the same BF16 (lower is better), with the share of tokens whose top-1 prediction matches the reference. ¹ Better than UD-IQ4_XS, but at 14.26 GiB it does not leave room for MTP plus a useful context on a 16 GB card.

• GLM-4.7-Flash-abliterated-12GB-GGUF↗

  cognitivers

• Llama-3.3-70B-Instruct-FP8-Dynamic↗

  cortecs

  This is a quantization of the Llama-3.3-70B-Instruct. The Meta Llama 3.3 is a state-of-the-art multilingual large language model (LLM) with 70 billion parameters, pretrained and instruction-tuned for exceptional performance in generative text-based tasks. Optimized for multilingual dialogue, it supports English and seven additional languages: French, German, Hindi, Italian, Portuguese, Spanish, and Thai, enabling seamless communication across diverse audiences. The model consistently outperforms both open-source and proprietary chat models on key industry benchmarks, delivering superior quality, safety, and helpfulness. Its advanced features and multilingual support position Llama 3.3 as a powerful tool for building innovative AI applications. This model provides an accuracy recovery of 99.67%. We did not check for data contamination. Evaluation was done using Eval. Harness with `limit=1000`.

• GLM-4.6V-AWQ-4bit↗

  cyankiwi