ct2fast-opus-mt-en-fr
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85003
ct2fast-opus-mt-es-en
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查看详情 ↗Qwen3-Embedding-0.6B-auto
michaelfeilThe Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
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查看详情 ↗Qwen3-Reranker-0.6B-seq
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查看详情 ↗rune-26b-a4b
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查看详情 ↗albert-base-rci-wikisql-row
michaelrglass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 ↗Qwen3.5-0.8B-NVFP4-MTP-GGUF
michaelw9999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 ↗Qwen3.6-27B-NVFP4-MTP-GGUF
michaelw9999Recently re-quantized with the latest version of advanced-gguf-quantizer tool. MTP included. This latest (6-June-2026) NVFP4 quant of Qwen3.6-27B keeps quality very high, while improving KLD, tail KLD metrics, top-token stability, and probability error compared to the earlier releases. It is faster and smaller in size. It applies my RSF scale fitting technique to the Q_K quants and uses a different tensor mix layout compared to previously. 6-June-2026: changed the MTP tensors to be NVFP4, ~130tk/s tg is now possible in some configurations on 5090. I am experimenting with a smaller verison of this model designed to be used on machines with 16GB of VRAM: Qwen3.6-27B-NVFP4-SMALL-MTP-GGUF . The SMALL version will be slower than this model but should maintain similar quality, although I am still evaluating it.
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查看详情 ↗Qwen3.6-35B-A3B-NVFP4-MTP-GGUF
michaelw9999This repo contains two experimental NVFP4 GGUF quantizations of **Qwen3.6-35B-A3B** for `llama.cpp`. This was quantized using my experimental advanced-gguf-quantizer tool. Both models were imatrix calibrated for the first time using a new custom dataset that I am evaluating. All PPL/KLD results were measured against the same BF16 wikitest KLD base, and then compared to the official NVFP4 release by NVIDIA. Further evaluation tests are underway to identify real world performance differences between **TURBO** and **HQ**.
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查看详情 ↗Qwopus3.6-27B-Coder-MTP-NVFP4-GGUF
michaelw9999This repo contains two experimental NVFP4 GGUF quantizations of **Jackrong**'s excellent Qwopus3.6-27-Coder for llama.cpp. This was quantized using my experimental advanced-gguf-quantizer tool. This model did **not** have any imatrix used with it, to better keep with the original model's finetuning done by Jackrong. Further evaluation tests are underway to identify real world performance differences between **TURBO** and **HQ**.
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查看详情 ↗Qwopus3.6-27B-v2-MTP-NVFP4-GGUF
michaelw9999This is the second release of the NVFP4 version of Jackrong's Qwopus3.6-27B-v2-MTP-GGUF. 9-June-2026: Fixed MTP heads to NVFP4; performance with MTP is much improved (108tk/s tg) Please note, I am not affiliated; this is my own quantization effort made with my experimental work-in-progress advanced-gguf-quantizer. More evaluatlions will be underway, this page will be updated when those are complete. Feedback on how to improve the quantizer/this quantization is appreciated. For improved performance and quality, try my llama.cpp NVFP4-Repack with MXFP6 from: https://github.com/michaelw9999/llama.cpp/tree/nvfp4repack_mxfp6_cuda These branches are updated regularly. NVFP4 repack preloads all tensors into a CUDA tile to boost speed. It is a tiny bit slower on first load, then provides ~10% prefill boost with a small reduction in token gen seen on larger models, and an increase on smaller models. However, it also enables NVFP4 input scale , which boosts model correctness.
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查看详情 ↗roboengine-sam
michaelyuanqwq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 ↗OvisOCR3-GGUF
michalland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 ↗Ollama-3.2-GGUF
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查看详情 ↗emotion_text_classifier
michelleli99DistilRoBERTa-base is a transformer model that performs sentiment analysis. I fine-tuned the model on transcripts from the Friends show with the goal of classifying emotions from text data, specifically dialogue from Netflix shows or movies. The model predicts 6 Ekman emotions and a neutral class. These emotions include anger, disgust, fear, joy, neutrality, sadness, and surprise. The model is a fine-tuned version of Emotion English DistilRoBERTa-base and DistilRoBERTa-base. This model was initially trained on the following table from Emotion English DistilRoBERTa-base: It was fine-tuned on: |Name|anger|disgust|fear|joy|neutral|sadness|surprise| |---|---|---|---|---|---|---|---| |Emotion Lines (Friends)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
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查看详情 ↗inappropriate_text_classifier
michelleli99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 ↗NSFW_text_classifier
michelleli99DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). It was fine-tuned on 14317 Reddit posts pulled from the (Reddit API) [https://praw.readthedocs.io/en/stable/].
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查看详情 ↗BioLinkBERT-base
michiyasunagaBioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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查看详情 ↗BioLinkBERT-large
michiyasunagaBioLinkBERT-large model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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查看详情 ↗LinkBERT-base
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查看详情 ↗LinkBERT-large
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查看详情 ↗kb-whisper-coreml
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查看详情 ↗QWEN-EDIT_360
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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。