DeepSeek-GRM-16B
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CountEX-KC
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View details ↗cevher-2-test-1-GGUF
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View details ↗cevher-test-10-lc-it-4-GGUF
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View details ↗JaColBERTv2
bclavieCurrent early checkpoint is fully functional and outperforms multilingual-e5-large, BGE-M3 and JaColBERT in early results, but full evaluation TBD.# Intro > There is currently no JaColBERTv2 technical report. For an overall idea, you can refer to the JaColBERTv1 arXiv Report Welcome to JaColBERT version 2, the second release of JaColBERT, a Japanese-only document retrieval model based on ColBERT.
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View details ↗tiny-llava-v1-hf
bczhou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 ↗Qwen3-0.6B-finetuned
bd2lcco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 ↗tibetan-ocr
BDRC**A vision-first OCR model for Tibetan pecha** — modern publications, woodblock prints, and manuscripts (uchen and u-med), including pecha layout and orthographic shorthands. Built by the Buddhist Digital Resource Center (BDRC) and trained on the `bec_mixed_elie_v8` mix of curated real + synthetic pages, with the **evaluation-benchmark pages held out of training**. - **Repository:** `BDRC/tibetan-ocr` (display name **Yigdzin 1**) - **Base model:** `PaddlePaddle/PaddleOCR-VL-1.6` (Apache-2.0) - **Live demo:** ocr.bdrc.io · **Desktop app:** buda-base/tibetan-ocr-app On a 1,070-page hand-transcribed benchmark (production serving: vLLM + sequential regime + DRY guard + temperature-retry), **median page CER is ~1.4%** and clean-page (non-catastrophic) mean CER is **~4.0%**, with **0 hard repetition loops**. The benchmark pages are held out of training. It is a *specialized* OCR model, not a general chat VLM.
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View details ↗Acestep1.5-qinglong-lokr
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View details ↗qinglong_controlnet-lllite
bdsqlsz和其他模型不同，我需要简单解释一下tile模型的用法。 总的来说，tile模型有三个用法， 1、不输入任何提示词，它可以直接还原参考图的大致效果，然后略微重新修改局部细节，可以用于V2V。(图2) 2、权重设定为0.55~0.75,它可以保持原本构图和姿势的基础上，接受提示词和LoRA的修改。(图3) 3、使用配合放大效果，对每个tiling进行细节增加的同时保持一致性。(图4) Unlike other models, I need to briefly explain the usage of the tile model. In general, there are three uses for the tile model, 1. Without entering any prompt words, it can directly restore the approximate effect of the reference image and then slightly modify local details. It can be used for V2V (Figure 2). 2. With a weight setting of 0.55~0.75, it can maintain the original composition and pose while accepting modifications from prompt words and LoRA (Figure 3). 3. Use in conjunction with magnification effects to increase detail for each tiling while maintaining consistency (Figure 4). Since the dataset used during training is an anime 2D/2.5D model, currently, its repainting effect on real photography styles is not good; we will have to wait until completing its final version.
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View details ↗stable-diffusion-xl-anime-V5
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View details ↗ttga1
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View details ↗ttga3
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View details ↗Qwen3.8-27B-Q3_K_XL-abliterated
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View details ↗sentence-transformers-multilingual-e5-small
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View details ↗Qwen3-VL-Reranker-2B-GGUF
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View details ↗GLM-5.3-Flash-Sushi-2.4bpw
beamster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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