Chroma-GGUF
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84991
resnet18-eurosat
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查看详情 ↗resnet50-eurosat
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查看详情 ↗bloomz-560m-nli
cmarkeaWe introduce the Bloomz-560m-NLI model, fine-tuned from the Bloomz-560m-chat-dpo foundation model. This model is trained on a Natural Language Inference (NLI) task in a language-agnostic manner. The NLI task involves determining the semantic relationship between a hypothesis and a set of premises, often expressed as pairs of sentences. The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B?) and is a classification task (given two sentences, predict one of the three labels). If sentence A is called *premise*, and sentence B is called *hypothesis*, then the goal of the modelization is to estimate the following: $$P(premise=c\in\{contradiction, entailment, neutral\}\vert hypothesis)$$ It should be noted that hypotheses and premises are randomly chosen between English and French, with each language combination representing a probability of 25%.
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查看详情 ↗detr-layout-detection
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查看详情 ↗distilcamembert-base
cmarkeaWe present a distillation version of the well named CamemBERT, a RoBERTa French model version, alias DistilCamemBERT. The aim of distillation is to drastically reduce the complexity of the model while preserving the performances. The proof of concept is shown in the DistilBERT paper and the code used for the training is inspired by the code of DistilBERT. The training for the distilled model (student model) is designed to be the closest as possible to the original model (teacher model). To perform this the loss function is composed of 3 parts: * DistilLoss: a distillation loss which measures the silimarity between the probabilities at the outputs of the student and teacher models with a cross-entropy loss on the MLM task ; * CosineLoss: a cosine embedding loss. This loss function is applied on the last hidden layers of student and teacher models to guarantee a collinearity between them ; * MLMLoss: and finaly a Masked Language Modeling (MLM) task loss to perform the student model with the original task of the teacher model. The final loss function is a combination of these three losses functions. We use the following ponderation:
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查看详情 ↗distilcamembert-base-ner
cmarkeaWe present DistilCamemBERT-NER, which is DistilCamemBERT fine-tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by Jean-Baptiste/camembert-ner based on the CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which **divides the inference time by two** with the same consumption power thanks to DistilCamemBERT. This model performance is compared to 2 reference models (see below) with the metric f1 score. For the mean inference time measure, an AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used:
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查看详情 ↗distilcamembert-base-nli
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查看详情 ↗We present DistilCamemBERT-Sentiment, which is DistilCamemBERT fine-tuned for the sentiment analysis task for the French language. This model is built using two datasets: Amazon Reviews and Allociné.fr to minimize the bias. Indeed, Amazon reviews are similar in messages and relatively shorts, contrary to Allociné critics, who are long and rich texts. This modelization is close to tblard/tf-allocine based on CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which **divides the inference time by two** with the same consumption power thanks to DistilCamemBERT. The dataset comprises 204,993 reviews for training and 4,999 reviews for the test from Amazon, and 235,516 and 4,729 critics from Allocine website. The dataset is labeled into five categories:
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查看详情 ↗SeedVR2-GGUF
cmekaThis is a GGUF quantization of SeedVR2 using llama.cpp and a custom patch (lcpp-seedvr.patch) as described in ComfyUI-GGUF Install the official SeedVR ComfyUI node and place the model in the "models/SEEDVR2" folder. https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler?tab=readme-ov-file#-installation
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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 ↗Lilith_AI_8B
CMM7590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 ↗acestep-5Hz-lm-4B-GGUF
cmp-nct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 ↗demodokos-foundry-music-v4
cmp-nct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 ↗demodokos-foundry-tts-v4
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查看详情 ↗llava-1.6-gguf
cmp-nctUpdate: PR is merged, llama.cpp now natively supports these models Important: Verify that processing a simple question with any image at least uses 1200 tokens of prompt processing, that shows that the new PR is in use. If your prompt is just 576 + a few tokens, you are using llava-1.5 code (or projector) and this is incompatible with llava-1.6 **note** Keep in mind the different fine tunes as described in the llama.cpp llava readme, it's essential to use the non defaults for non vicuna models The mmproj files are the embedded ViT's that came with llava-1.6, I've not compared them but given the previous releases from the team I'd be surprised if the ViT has not been fine tuned this time. If that's the case, using another ViT can cause issues. You need to use the mmproj of the right model but you can mix quantizations.
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查看详情 ↗Qwen3-14B-GGUF
cmp-nct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 ↗Qwen3.5-27B-GGUF
cmp-nct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 ↗Qwen3.5-35B-A3B-GGUF
cmp-nct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 ↗Qwen3.5-4B-GGUF
cmp-nct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 ↗Qwen3.5-9B-GGUF
cmp-nct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-GGUF
cmp-nct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 ↗Qwen3.6-35B-A3B-GGUF
cmp-nct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 ↗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。