Hatsune_Miku__RVC_v2_
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latina_girl-female
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View details ↗RussianTeenageGirlVoice
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View details ↗soft_asmr_female
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View details ↗Softgirl
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View details ↗wendyrv
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View details ↗YoungEgirl
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View details ↗IndicVoice-82M
Bindkushal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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BindWeave
字节跳动基础档案已收录，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 ↗dots.mocr-FP8
binedge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 ↗lynx-instruct-30b
bineric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 ↗NorskGPT-Llama3-8b
bineric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 ↗dolus-v3-ep1-instruct-GGUF
bingbangboom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 ↗Qwen3006B-transcriber-beta
bingbangboom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_hybrid_fl2va_ref2va_b25-49_w4a8_mixed
binglingzhimengA 4-bit (`asym_w4a8_int8`) build of the community-standard **b25-49** hybrid — the variant that, as far as I could find, did not exist in W4A8 form before this upload. Single file, drop-in for ComfyUI's stock **Load Diffusion Model**. A tensor-by-tensor comparison shows the two are effectively identical everywhere **except** the per-block `adaln_proj` weights (the AdaLN modulation projections that route conditioning signal into each block). The `bX-49` hybrids keep fl2va as the base and take `adaln_proj` from ref2va for **blocks X–49 only**. `b25-49` = blocks 25–49 from ref2va (the community default balance point). `b30-49` leans quality, `b20-49`/`b15-49` lean reference adherence.
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View details ↗minimax_h3_hybrid_fl2va_ref2va_b25-49_w6a8
binglingzhimengA **6-bit weight / 8-bit activation** build of the community-standard **b25-49** hybrid. Single file, drop-in for ComfyUI's stock **Load Diffusion Model**. Between the two extreme choices: W4A8 (11.68 GiB, coarser 4-bit) and INT8 (20.97 GB, biggest). Per Comfy-Org's own `comfy-kitchen` notes, the 6-bit path trades **1.5× the bytes of 4-bit for ~3× lower weight error** — it is the accuracy-leaning middle of the H3 quantization family. - **fl2va** — first/last-keyframe conditioning, noticeably better visual/audio quality, no reference slots. - **ref2va** — adds image/video/audio reference conditioning, slightly lower base quality.
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View details ↗adetailer
BingsuSince `getattr` is classified as a dangerous pickle function, any segmentation model that uses it is classified as unsafe. All models were created and saved using the official ultralytics library, so it's okay to use files downloaded from a trusted source.
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View details ↗clip-vit-base-patch32-ko
BingsuKorean CLIP model trained by Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation로 학습된 한국어 CLIP 모델입니다. https://github.com/huggingface/transformers/blob/bc21aaca789f1a366c05e8b5e111632944886393/src/transformers/models/clip/modeling_clip.py#L661-L666
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View details ↗Korean CLIP model trained by Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation로 학습된 한국어 CLIP 모델입니다.
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View details ↗exaone-3.0-7.8b-it
Bingsu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 ↗llama-190m-arch
Bingsu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 ↗yolo-world-mirror
Bingsuhttps://docs.ultralytics.com/models/yolo-world/#available-models-supported-tasks-and-operating-modes
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View details ↗binomial-shannon-2
BinomialTechnologiesA **financial news characterizer with two modes**: it reads *ticker-tagged* company news the way binomial-shannon-1 does (19 structured features), and reads *macro* news (central banks, inflation, rates, FX, commodities, geopolitics) with a dedicated 35-output macro head bank. A built-in router selects the right head set per article. ~15-30 ms on CPU. **Ticker mode (19 features, identical to shannon-1)** — event type (10 binary), tone, implied_direction, novelty, claim_type (4), specificity, materiality_if_true. Every macro head is a softmax or a signed scalar — argmax for a label, the weighted score for a continuous summary, or the entropy for uncertainty.
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