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Explore AI models 84991
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Danswer基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗first_model
danturner1982基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗phonon-31m-en-GGUF
danyalmalik基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LTX-2.3-GGUF
daobuliao基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LTX-2.3-Multiple-Subject-Reference
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View details ↗Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic
dapinskiwomin基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sorsolingo-mt-en-bsl-test
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View details ↗**English summary.** A 4-step, CFG-free adapter for OpenMOSS-Team/MOSS-Image-SFT (50 steps × 2 forwards with CFG 3). It continues lodestones/MOSS-Image-SFT-8-steps-dmd (data-space DMD, arXiv 2610.12102) restricted to 4 iterations, trained with DMAD (arXiv 2610.02188): a LoRA critic on the frozen teacher separates teacher samples from student samples, and the student minimises the negative critic logit. DiT time per 1024² image on one H100: 16.2 s (teacher) → 0.81 s. Compared with the lodestones checkpoint run for 4 steps, images have more detail and livelier composition, with some regressions (see Results). Same file format and sampler as the lodestones checkpoint; `inference.py` runs it with the release pipeline. Training code is in `code/`. MOSS-Image-SFT(50 ステップ・CFG 3 で 1 枚 100 回の DiT 計算)を、**4 回の計算・CFG なし**で画像を出せるようにする高速化アダプタです。 lodestones の 8 ステップ版(data-space DMD、update 2600)を出発点に、4 ステップに絞って DMAD で追加学習しました。 カスタムノード `comfyui/rapid-moss/` を同梱しています。ComfyUI 本体も MOSS-Image に対応していますが、専用サンプラーと time path の置き換えはなく、標準の `LoraLoaderModelOnly` は LoRA を bf16 の重みに足し込みます(当たる差分の相対誤差 81%)。標準ノードだけでも絵は出ますが、学習した生成器とは別物になります(下の比較)。このノードを使ってください。
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View details ↗Muse-Spark-Open-816B-A42B-oQ4e
dareallest207基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mineral-classification-deit
DariaZielinskaThe model is based on `facebook/deit-tiny-patch16-224` and was fine-tuned on a subset of the MineralImage5K-98 dataset. For this experiment, 10 mineral classes were selected. The training set was balanced to 282 images per class, resulting in 2820 training images. The model was trained on a relatively small subset of the original dataset and only covers 10 mineral classes. Performance may therefore be lower on minerals, image conditions, or classes not represented in the training data.
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View details ↗bge-m3-es-legal-tmp-6
dariolopez基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The trained model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne focused on question/answer using MS-MARCO dataset translated into Spanish (query - positive - negative - negative - negative - negative) dataset to train.
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View details ↗Lightning-4step-Qwen-Image-2.1-GGUF
darioooooo0o基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗NVIDIA-Nemotron-3.5-Bolt-74B-A9B-GGUF
darioooooo0oGGUF quants of nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16 for llama.cpp: a 74B Mamba-2 / Transformer hybrid with LatentMoE (9B active) and a C-RADIO v4-H vision encoder. NVIDIA's own GGUF is a single 2-bit file trained for that precision (the 24 GB QAD checkpoint). These files are quantized from the full-precision BF16 weights instead, at 3 to 8 bits, for machines with more memory: 2 files from 28 to 45 GiB, plus the vision projector. The routed experts are ~89% of the weights, so every file runs on a GPU of any size by keeping some experts in system RAM. Rows: total memory you need = VRAM + RAM. Pick the largest file whose RAM column fits your machine.
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View details ↗Qwen3.6-35B-A3B-TinyKV
darioooooo0o基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.6-35B-A3B-TinyKV-GGUF
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View details ↗Spark-X2.5-1.7B-Abliterated-GGUF
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View details ↗Qwen3-4B-Algerian-Darja
DarjaCore基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MiniMax-H3-Turbo-Lora
DARK-MING基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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