Qwen3.8-27B-SLQ-EAR99
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探索 AI 模型 84999
Qwen3.8-27B-SLQ-GGUF
kaitchupThis is Qwen/Qwen3.8-27B, compressed with a **customized version of SLQ (Statistically-Lossless Quantization)** and assembled as GGUF. The search combines existing quantized donor weights and chooses a source separately for 498 complete matrices, including embeddings and the output head. The filename suffixes **Q5_K_S**, **Q5_K_M**, and **Q6_K** are Hub compatibility and dominant-precision-family labels for these **custom SLQ allocations**. The search considered **UD-Q4_K_XL, UD-Q5_K_XL, UD-Q6_K_XL, Q4_0, Q8_0, and BF16** from unsloth/Qwen3.8-27B-GGUF, plus **IQ3_S** from ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF. Identical tensor payload alternatives were canonicalized. The final three models select no GSQ donor tensors; its candidates remained eligible during search.
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查看详情 ↗Ornith-1.5-122B-DFlash2-GGUF
kaivossThis model was converted to GGUF format from `incoai/Ornith-1.5-122B-DFlash2` using upstream llama.cpp (`ead9f32`). This is a **DFlash 2 draft model** (0.9B, block-diffusion drafter) for `ornith-ai/Ornith-1.5-122B`. It is not a standalone model. It reuses the target's token embeddings and LM head at runtime, so load it with `-md` next to an Ornith-1.5-122B GGUF. The following quants are available: BF16, F16, F32, IQ3_M, IQ3_S, IQ4_NL, IQ4_XS, MXFP4_MOE, Q1_0, Q2_0, Q2_K, Q3_K, Q3_K_L, Q3_K_M, Q3_K_S, Q4_0, Q4_1, Q4_K, Q4_K_M, Q4_K_S, Q5_0, Q5_1, Q5_K, Q5_K_M, Q5_K_S, Q6_K, Q8_0, TQ1_0, TQ2_0
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查看详情 ↗DiffLlama-0.3B-handcut
kajuma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 ↗vits-ljs
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查看详情 ↗vits-vctk
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查看详情 ↗kf-deberta-base
kakaobank**금융도메인 벤치마크** | Model | FN-Sentiment (v1) | FN-Sentiment (v2) | FN-Adnews | FN-NER | KorFPB | KorFiQA-SA | KorHeadline | Avg (FiQA-SA 제외) | |:-------------------:|:-----------------:|:-----------------:|:---------:|:---------:|:---------:|:----------:|:-----------:|:-----------------:| | | ACC | ACC | ACC | F1-micro | ACC | MSE | Mean F1 | | | KLUE-RoBERTa (Base) | 98.26 | 91.21 | 96.34 | 90.31 | 90.97 | 0.0589 | 81.11 | 94.03 | | KoELECTRA (Base) | 98.26 | 90.56 | 96.98 | 89.81 | 92.36 | 0.0652 | 80.69 | 93.90 | | KF-DeBERTa (Base) | **99.36** | **92.29** | **97.63** | **91.80** | **93.47** | **0.0553** | **82.12** | **95.27** | **범용도메인 벤치마크** | Model | NSMC | PAWS | KorNLI | KorSTS | KorQuAD | Avg (KorQuAD 제외) | |:-------------------:|:---------:|:---------:|:---------:|:---------:|:---------------:|:----------------:| | | ACC | ACC | ACC | spearman | EM/F1 | | | KLUE-RoBERTa (Base) | 90.47 | 84.79 | 81.65 | 84.40 | 86.34/94.40 | 85.33 | | KoELECTRA (Base) | 90.63 | 84.45 | 82.24 | 85.53 | 84.83/93.45 | 85.71 | | KF-DeBERTa (Base) | **91.36** | **86.14** | **84.54** | **85.99** | **86.60/95.07** | **87.01** | KF-DeBERTa의 소스코드 및 모델은 MIT 라이선스 하에 공개되어 있습니다. 라이선스 전문은 MIT 파일에서 확인할 수 있습니다. 모델의 사용으로 인해 발생한 어떠한 손해에 대해서도 당사는 책임을 지지 않습니다.
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查看详情 ↗align-base
kakaobrainThe ALIGN model was proposed in "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision" by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. ALIGN features a dual-encoder architecture with EfficientNet as its vision encoder and BERT as its text encoder, and learns to align visual and text representations with contrastive learning. Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe. The code for ALIGN was not publicly released, the base model is converted from the original implementation of the Kakao Brain team. This implementation follows the same architecture and hyperparameters as provided in the original Google model but is trained on the open source COYO dataset. Google’s ALIGN model, while trained on a huge dataset of 1.8 billion image-text pairs, cannot be replicated as the datasets is not public. Kakao Brain's ALIGN is on-par or outperforms Google ALIGN's reported metrics despite being trained on the much smaller, albeit carefully curated COYO-700M dataset. COYO is available on the hub as a dataset.
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查看详情 ↗kanana-1.5-15.7b-a3b-base
kakaocorp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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kakaocorp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 ↗kanana-1.5-2.1b-instruct-2505
kakaocorp`Kanana 1.5`, a newly introduced version of the Kanana model family, presents substantial enhancements in **coding, mathematics, and function calling capabilities** over the previous version, enabling broader application to more complex real-world problems. This new version now can handle __up to 32K tokens length natively and up to 128K tokens using YaRN__, allowing the model to maintain coherence when handling extensive documents or engaging in extended conversations. Furthermore, Kanana 1.5 delivers more natural and accurate conversations through a __refined post-training process__. Models MT-Bench KoMT-Bench IFEval HumanEval+ MBPP+ GSM8K (0-shot) MATH MMLU (0-shot, CoT) KMMLU (0-shot, CoT) FunctionChatBench > [!Note] > \* Models released under Apache 2.0 are trained on the latest versions compared to other models.
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查看详情 ↗kanana-1.5-8b-instruct-2505
kakaocorp`Kanana 1.5`, a newly introduced version of the Kanana model family, presents substantial enhancements in **coding, mathematics, and function calling capabilities** over the previous version, enabling broader application to more complex real-world problems. This new version now can handle __up to 32K tokens length natively and up to 128K tokens using YaRN__, allowing the model to maintain coherence when handling extensive documents or engaging in extended conversations. Furthermore, Kanana 1.5 delivers more natural and accurate conversations through a __refined post-training process__. Models MT-Bench KoMT-Bench IFEval HumanEval+ MBPP+ GSM8K (0-shot) MATH MMLU (0-shot, CoT) KMMLU (0-shot, CoT) FunctionChatBench > [!Note] > \* Models released under Apache 2.0 are trained on the latest versions compared to other models.
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查看详情 ↗kanana-1.5-v-3b-instruct
kakaocorp- Kanana-1.5-v-3b-instruct - Intended Use - Model Details - Evaluation - Model Configuration Summary - Overview - Image Benchmarks (EN) - Image Benchmarks (KO) - Multimodal Instruction Following Benchmarks (EN, KO) - Note on Benchmarking Methodology - Usage - Requirements - Quickstart - Limitations - Contributors - Contact The Unified Foundation Model (UFO) task force of Kanana at Kakao developed and released the Kanana-V family of multimodal large language models (MLLMs), a collection of pretrained text/image-to-text (TI2T) models. kanana-1.5-v-3b-instruct is intended for research and application development in multimodal understanding and text generation tasks. Typical use cases include image captioning, document understanding, OCR-based reasoning, and multimodal instruction following in both English and Korean. The model is optimized for both general-purpose and Korea-specific benchmarks, making it suitable for bilingual environments.
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查看详情 ↗kanana-2-30b-a3b-instruct
kakaocorp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 ↗kanana-2-30b-a3b-instruct-2601
kakaocorp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 ↗kanana-2-30b-a3b-thinking-2601
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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。