Sana_1600M_1024px_BF16_diffusers_ch5632
Lawrence-cj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 ↗DISCOVER & COMPARE
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noobai-XL-0.5
Laxhar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 ↗noobai-XL-0.75
Laxhar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 ↗noobai-XL-1.0
LaxharIt utilizes the latest full Danbooru and e621 datasets for training, with native tags caption. - CFG: 5 ~ 6 - Steps: 25 ~ 30 - Sampling Method:Euler a - Resolution: Total area around 1024x1024. Best to choose from: 768x1344, **832x1216**, 896x1152, 1024x1024, 1152x896, 1216x832, 1344x768 - Data normalization based on various sources and ratings. - Application of time-based decay coefficients according to date recency. - Ranking of images within the entire dataset based on this processing.
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View details ↗noobai-XL-1.1
LaxharIt utilizes the latest full Danbooru and e621 datasets for training, with native tags caption. - CFG: 5 ~ 6 - Steps: 25 ~ 30 - Sampling Method:Euler a - Resolution: Total area around 1024x1024. Best to choose from: 768x1344, **832x1216**, 896x1152, 1024x1024, 1152x896, 1216x832, 1344x768 - Data normalization based on various sources and ratings. - Application of time-based decay coefficients according to date recency. - Ranking of images within the entire dataset based on this processing.
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View details ↗noobai-XL-Vpred-0.5
Laxhar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 ↗noobai-XL-Vpred-0.6
Laxhar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 ↗noobai-XL-Vpred-0.75
Laxhar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 ↗noobai-XL-Vpred-0.9r
Laxhar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 ↗noobai-XL-Vpred-1.0
LaxharThis image generation model, based on Laxhar/noobai-XL_v1.0, leverages full Danbooru and e621 datasets with native tags and natural language captioning. Implemented as a v-prediction model (distinct from eps-prediction), it requires specific parameter configurations - detailed in following sections. Special thanks to my teammate euge for the coding work, and we're grateful for the technical support from many helpful community members.
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View details ↗CCPFN
Layer6This repository contains the weights for **CCPFN** (Continuous Causal Prior-Fitted Network), the first causal foundation model for continuous treatment settings, as presented in the paper Causal Foundation Models with Continuous Treatments. By leveraging in-context learning, CCPFN estimates the *conditional expected potential outcome* (CEPO), defined as $𝔼[Y(t) \mid X = x]$, predicting causal effects across a wide variety of unseen tasks without any additional training or fine-tuning.
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View details ↗TabDPT
Layer6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 ↗seethroughv0.0.1_marigold
layerdifforgThe pseudo-depth model of See-through, fine-tuned from Marigold Depth v1.1 for anime characters. It is used together with the LayerDiff 3D model to order the decomposed layers. Read our GitHub repository for usage and details. A 4-bit NF4 version for GPUs with less memory is available at 24yearsold/seethroughv0.0.1_marigold_nf4. The See-through code is licensed under Apache-2.0. These weights are released under Apache-2.0 for our own contributions, and they also inherit the licences of the models they are derived from:
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View details ↗seethroughv0.0.2_layerdiff3d
layerdifforgThis is the model file for See-through with the new tag definition. It generates the transparent body-part layers in the See-through pipeline, together with the depth model. Read our GitHub repository for usage and details. A 4-bit NF4 version for GPUs with less memory is available at 24yearsold/seethroughv0.0.2_layerdiff3d_nf4. The See-through code is licensed under Apache-2.0. These weights are released under Apache-2.0 for our own contributions, and they also inherit the licences of the models they are derived from:
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View details ↗AMALIA-9B-0626-DPO-GGUF
layerx-labsGGUF quantizations of amalia-llm/AMALIA-9B-0626-DPO for use with llama.cpp and compatible runtimes (LM Studio, Ollama, Jan, etc.). AMALIA is an open-source language model targeting European Portuguese, developed by a consortium of Portuguese universities and research centres and funded by the Government of Portugal. This is the DPO (preference-tuned) variant. See the original model card and the technical report for full details. Converted with `convert_hf_to_gguf.py` (llama.cpp b8470) from the original bf16 weights. BF16 and Q4_K_S come directly from the original weights; Q8_0 was converted directly from bf16 and Q4_K_M was requantized from that Q8_0 (Q8_0 is near-lossless relative to bf16). The chat template (ChatML with the AMALIA Portuguese system prompt) is embedded in the GGUF metadata.
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View details ↗AMALIA-9B-0626-SFT-GGUF
layerx-labs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 ↗ReAligned-Qwen3.5-0.8B-GGUF
Lazarus-Ai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 ↗ReAligned-Qwen3.5-27B-GGUF
Lazarus-Ai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 ↗ReAligned-Qwen3.5-2B-GGUF
Lazarus-Ai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 ↗ReAligned-Qwen3.5-35B-A3B-GGUF
Lazarus-Ai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 ↗ReAligned-Qwen3.5-4B-GGUF
Lazarus-Ai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 ↗ReAligned-Qwen3.5-9B-GGUF
Lazarus-Ai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 ↗all-indo-e5-small-v4
LazarusNLPThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗NusaBERT-base
LazarusNLP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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