MobileMoE-M-Base
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MobileMoE-S-Base
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View details ↗TR-finetuned-adversary
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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗FineLlama-3.1-8B-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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View details ↗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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View details ↗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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View details ↗gemma-3-12b-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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View details ↗gemma-3-12b-it-abliterated-v2
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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View details ↗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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View details ↗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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