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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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84991
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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗**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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。