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AI 모델 84759

• Cosmos-1.0-Diffusion-7B-Text2World↗

  Arsh9210

• L3-8B-Stheno-v3.2-exl2↗

  bartowski

• llama-3-Nephilim-v3-8B-GGUF↗

  bartowski

• p-e-w_gpt-oss-20b-heretic-GGUF↗

  bartowski

  All quants made using imatrix option with dataset from here combined with a subset of combined_all_small.parquet from Ed Addario here No chat template specified so default is used. This may be incorrect, check original model card for details. 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.

• Qwerus-7B-GGUF↗

  bartowski

• qwenar-0.6b-montevideo↗

  brivangl

• canvitb16-add-vpe-finetune-g128px-s512px-in1k-from-in1k-2026-07-24↗

  canvit

• tiny-aya-en-thinker↗

  Cohere

• stable-diffusion-safety-checker↗

  CompVis

  - **Developed by:** More information needed - **Shared by [Optional]:** CompVis - **Model type:** Image Identification - **Language(s) (NLP):** More information needed - **License:** More information needed - **Parent Model:** CLIP - **Resources for more information:** - CLIP Paper - Stable Diffusion Model Card The CLIP model devlopers note in their model card : We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.

• Qwen3.6-Qworus-35B-A3B-V1-APEX-GGUF↗

  DarkKitsune

  This is a DELLA merge of the following finetuned & post-trained models: | Model Repo | Purpose | | --- | --- | | EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF | The base model, for terminal use, coding, software architecture, planning. Also provides MTP support | | Kwaipilot/KAT-Coder-V2.5-Dev | Contributes efficient coding with fewer tokens over more languages | | Accio-Lab/occamy-1.0 | Contributes general agent abilities, also a semi-official finetune | | AllSpark-Research/CompoWorld | Also contributes general agent abilities, different strengths/weaknesses from occamy | The result is an intelligent and efficient hybrid model for coding, terminal agent work, planning, designing, and conversation. Although most of this model's weights were merged with with a method that drops weights, more sensitive tensors such as the router gates and shared experts, were protected by using a density of 1.0 and epsilon of 0.0 to disable sparsification.