MobileMoE-M-Base
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探索 AI 模型 85003
MobileMoE-S-Base
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查看详情 ↗TR-finetuned-adversary
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查看详情 ↗Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time ⏳. Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. This model uses distilbert as its base model and fine-tunes it on the Inspec dataset.
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查看详情 ↗Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time ⏳. Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. This model uses KBIR as its base model and fine-tunes it on the Inspec dataset. KBIR or Keyphrase Boundary Infilling with Replacement is a pre-trained model which utilizes a multi-task learning setup for optimizing a combined loss of Masked Language Modeling (MLM), Keyphrase Boundary Infilling (KBI) and Keyphrase Replacement Classification (KRC). You can find more information about the architecture in this paper.
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查看详情 ↗AlphaMonarch-7B
mlabonne**tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉** AlphaMonarch-7B is a DPO fine-tuned of mlabonne/NeuralMonarch-7B using the argilla/OpenHermes2.5-dpo-binarized-alpha preference dataset. It is based on a merge of the following models using LazyMergekit: * mlabonne/OmniTruthyBeagle-7B-v0 * mlabonne/NeuBeagle-7B * mlabonne/NeuralOmniBeagle-7B
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查看详情 ↗Beyonder-4x7B-v3
mlabonneBeyonder-4x7B-v3 is an improvement over the popular Beyonder-4x7B-v2. It's a Mixture of Experts (MoE) made with the following models using LazyMergekit: * mlabonne/AlphaMonarch-7B * beowolx/CodeNinja-1.0-OpenChat-7B * SanjiWatsuki/Kunoichi-DPO-v2-7B * mlabonne/NeuralDaredevil-7B Special thanks to beowolx for making the best Mistral-based code model and to SanjiWatsuki for creating one of the very best RP models. This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).
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查看详情 ↗chesspythia-70m
mlabonne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 ↗ChimeraLlama-3-8B-v2
mlabonneChimeraLlama-3-8B-v2 is a merge of the following models using LazyMergekit: * NousResearch/Meta-Llama-3-8B-Instruct * mlabonne/OrpoLlama-3-8B * cognitivecomputations/dolphin-2.9-llama3-8b * Locutusque/llama-3-neural-chat-v1-8b * cloudyu/Meta-Llama-3-8B-Instruct-DPO * vicgalle/Configurable-Llama-3-8B-v0.3
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查看详情 ↗ChimeraLlama-3-8B-v3
mlabonneChimeraLlama-3-8B-v3 is a merge of the following models using LazyMergekit: * NousResearch/Meta-Llama-3-8B-Instruct * mlabonne/OrpoLlama-3-8B * cognitivecomputations/dolphin-2.9-llama3-8b * Danielbrdz/Barcenas-Llama3-8b-ORPO * VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct * vicgalle/Configurable-Llama-3-8B-v0.3 * MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3
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查看详情 ↗Daredevil-8B-abliterated
mlabonneIt based on the technique described in the blog post "Refusal in LLMs is mediated by a single direction". This is an uncensored model. You can use it for any application that doesn't require alignment, like role-playing. Daredevil-8B-abliterated is the second best-performing 8B model on the Open LLM Leaderboard in terms of MMLU score (27 May 24).
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查看详情 ↗FineLlama-3.1-8B-GGUF
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查看详情 ↗gemma-2b-GGUF
mlabonne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 ↗gemma-2b-it-GGUF
mlabonne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 ↗gemma-3-12b-it-abliterated-GGUF
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查看详情 ↗gemma-3-12b-it-abliterated-v2
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查看详情 ↗This is an uncensored version of google/gemma-3-12b-it created with a new abliteration technique. See this article to know more about abliteration. The refusal direction is computed by comparing the residual streams between target (harmful) and baseline (harmless) samples. The hidden states of target modules (e.g., o_proj) are orthogonalized to subtract this refusal direction with a given weight factor. These weight factors follow a normal distribution with a certain spread and peak layer. Modules can be iteratively orthogonalized in batches, or the refusal direction can be accumulated to save memory. Finally, I used a hybrid evaluation with a dedicated test set to calculate the acceptance rate. This uses both a dictionary approach and NousResearch/Minos-v1. The goal is to obtain an acceptance rate >90% and still produce coherent outputs.
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查看详情 ↗gemma-3-1b-it-abliterated-GGUF
mlabonne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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价格待核验多模态与渠道费用须分别核对
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。