rut5-small
cointegrated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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rut5-small-normalizer
cointegrated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 ↗SONAR_200_text_encoder
cointegratedThis is a port of the multilingual SONAR text encoder (https://huggingface.co/facebook/SONAR) to the `transformers` format from `fairseq2`. Its embeddings are expected be equal to those the official implementation (https://github.com/facebookresearch/SONAR), but the latter stays the source of truth. The encoder supports the same 202 languages as NLLB-200 (see also the source model card and FLORES-200 lang code mapping).
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View details ↗minimax-h3-int8-base-ref2va
cokeadrink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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Cola-DLM
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 ↗Chen-9B-GGUF
colawolfie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 ↗colbertv1.9
colbert-ir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 ↗colbertv2.0
colbert-irFigure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. As Figure 1 illustrates, ColBERT relies on fine-grained **contextual late interaction**: it encodes each passage into a **matrix** of token-level embeddings (shown above in blue). Then at search time, it embeds every query into another matrix (shown in green) and efficiently finds passages that contextually match the query using scalable vector-similarity (`MaxSim`) operators. These rich interactions allow ColBERT to surpass the quality of _single-vector_ representation models, while scaling efficiently to large corpora. You can read more in our papers:
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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 ↗**RVN** is a double-refined abliterated variant of **Qwen3.8-27B**, built on top of `trohrbaugh/Qwen3.8-27B-heretic-ara` (an ARA abliteration by **Tim Rohrbaugh**) and further refined with **two additional full-weight ARA passes** targeting residual refusals. It retains very low behavioral damage (KL ≈ 0.0085) while reducing harmful-prompt refusals from 3/100 (source) to **0–1/100** in independent measurements. > **Note on this repository's history.** This repo previously hosted the original > `Qwen3.8-27B-Heretic-Q4_K_M.gguf` (single-quant release from the earlier > `trohrbaugh/Qwen3.8-27B-heretic` source). That file is kept as **legacy** for > download-count continuity and backward compatibility — it is the *older* abliteration > variant and is superseded by the RVN files below. Prefer the RVN quants for new > deployments. > **Not for all audiences.** This model has reduced safety guardrails by design. It is > intended for adult audiences (18+) doing research, creative writing, roleplay, and > uncensored generation. Certain guardrails are intentionally left in place; use > responsibly and in accordance with your local laws.
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View details ↗CLASS_IT-140M
colinglabThe model is first pre-trained for 8 epochs on a cleaned version of the BabyLM Challenge strict track dataset. After pre-training, the model is instruction-tuned on two additional datasets (8.7M words total) for 10 epochs : The model has been submitted to the 2025 BabyLM Challenge – Interaction Track: https://huggingface.co/spaces/BabyLM-community/babylm-leaderboard-2025-all-tasks This model was introduced in the paper: **“CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for BabyLMs”** *(Capone, Bondielli & Lenci, BabyLM Challange 2025)* 📄 ArXiv: 2510.25364
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View details ↗whisper-tiny-hindi
collabora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 ↗AFM-4.5B
CollectionStudio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 ↗AFM-4.5B-Preview
CollectionStudio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-VL-Embedding-8B-W8A8
collin-park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 ↗gpt2-small-ja
colorfulscoop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 ↗lab1-cifar10-mobilenet
Columb0o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 ↗open-jev-deberta-v3-large
com-kotobalabs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 ↗YiSM-blossom5.1-34B-SLERP
CombinHorizon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 ↗Qwen3.8-27B-nvfp4full-NInfer
cometkimThe repository contains a fuller-NVFP4 weight profile of Qwen/Qwen3.8-27B in the native NInfer `.ninfer` artifact format, with the z-lab/Qwen3.8-27B-DFlash2 block-diffusion speculative drafter embedded in the same image. It runs on NInfer **v3** engines — Text and MTP components through format/shape routes the upstream runtime already implements, the optional NVFP4-encoded DFlash2 companion through the fork's runtime support. It is not a Transformers checkpoint, Safetensors distribution, or GGUF file. Compared with the official `nvfp4` profile it extends NVFP4 from the layers 0–55 MLP to nearly the whole Text backbone: - every Text `mlp/gate_up` and `mlp/down` is NVFP4 (the layers 0–55 words are copied bit-exactly from unsloth/Qwen3.8-27B-NVFP4; layers 56–63 are quantized locally from the official BF16 checkpoint); - every GDN `query_key_value_z` is NVFP4, and GDN `output` is NVFP4 on 47 of 48 layers; - full-attention `query_key_gate_value` is NVFP4 on the ten deepest layers and `attention/output` on 14/16, with 9 BF16 exception parents retaining the registered Qwen3.6-27B NVFP4 exception pattern; - the token embedding and full output head use groupwise W8G32_F16S instead of row-scaled FP8. This yields 247 NVFP4 parents with 247 site-level input divisors. Locally quantized parents use the documented encoder profile `NVFP4_MAXABS_DIVISOR_RNE_V1` (per-tensor FP32 divisor, per-16 E4M3FN block scales, RNE E2M1 codes) with site divisors calibrated by streaming the official BF16 checkpoint. The complete contract, encoder, calibration corpus, and validation evidence are `docs/maintainer/qwen3.8-27b-artifact.md` §14 in the fork.
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View details ↗Qwen3.8-27B-nvfp4qat-NInfer
cometkimThe repository contains a QAT-sourced NVFP4 weight profile of Qwen/Qwen3.8-27B in the native NInfer `.ninfer` artifact format, with the z-lab/Qwen3.8-27B-DFlash2 block-diffusion speculative drafter embedded in the same image. It runs on NInfer **v3** engines — Text and MTP components through format/shape routes the upstream runtime already implements, the optional NVFP4-encoded DFlash2 companion through the fork's runtime support. It is not a Transformers checkpoint, Safetensors distribution, or GGUF file. This is a fourth weight profile for the existing `qwen3_8_27b` target — a peer of the official `groupwise-int` and `nvfp4` profiles and of the fork's fuller-requant `nvfp4full` profile. The Text weight stack is copied **word-for-word** from QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4 — a quantization-aware-trained checkpoint (QUASAR loss-aware NVFP4 distillation against the frozen BF16 teacher, arXiv 2608.13966): - every one of the 496 text linear layers is NVFP4 (W4A4) — attention, gated-delta-net, and MLP alike, with no high-precision exceptions; - the QAT factory quantizes per site, so every constituent tensor of a fused parent shares one weight and input global scale; the converter enforces that sharing before copying a word, and the site input divisors ride the artifact directly; - the GDN control projections are decoded from their QAT NVFP4 words to the BF16 control parent the engine consumes.
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View details ↗ACE-Step
Comfy-OrgSee https://comfyanonymous.github.io/ComfyUI_examples/audio/ or https://docs.comfy.org/tutorials/audio/ace-step/ace-step-v1 for how to use it in ComfyUI. ACE Step v1 M2M Editing ACE-Step v1 Text to Instrumentals Music ACE Step v1 Text to Song
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View details ↗ACE-Step-1.5
Comfy-OrgThese are repackaged files to work with ComfyUI, original model repo is: https://huggingface.co/ACE-Step/Ace-Step1.5 ACE-Step 1.5XL Turbo: Text to Music ACE-Step 1.5XL SFT： Text to Music ACE-Step 1.5 Music Generation AIO ACE-Step 1.5XL Base: Text to Music ACE-Step 1.5 Music Generation Workflow ACE-Step 1.5 AI Lyric Song Generation ACE-Step 1.5 Music Generation (4B LLM )
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