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AI 모델 84759

• Moonfrost-777M-Instruct-v2↗

  whoashish115

• Huihui-gemma-4-12B-coder-fable5-composer2.5-v1-abliterated-GGUF↗

  XAEA12

• Xiangxin-2XL-Chat-1048k-Chinese-Llama3-70B↗

  xiangxinai

  We offer customized model training services. If you need to train industry-specific models, domain-specific models, or private models, please contact us at: wanglei@xiangxinai.cn. Xiangxin-2XL-Chat-1048k是象信AI基于Meta Llama-3-70B-Instruct模型和Gradient AI的扩充上下文的工作，利用自行研发的中文价值观对齐数据集进行ORPO训练而形成的Chat模型。该模型具备更强的中文能力和中文价值观，其上下文长度达到100万字。在模型性能方面，该模型在ARC、HellaSwag、MMLU、TruthfulQA_mc2、Winogrande、GSM8K_flex、CMMLU、CEVAL-VALID等八项测评中，取得了平均分70.22分的成绩，超过了Gradientai-Llama-3-70B-Instruct-Gradient-1048k。我们的训练数据并不包含任何测评数据集。 Xiangxin-2XL-Chat-1048k is a Chat model developed by Xiangxin AI, based on the Meta Llama-3-70B-Instruct model and expanded context from Gradient AI. It was trained using a proprietary Chinese value-aligned dataset through ORPO training, resulting in enhanced Chinese proficiency and alignment with Chinese values. The model has a context length of up to 1 million words. In terms of performance, it surpassed the Gradientai-Llama-3-70B-Instruct-Gradient-1048k model with an average score of 70.22 across eight evaluations including ARC, HellaSwag, MMLU, TruthfulQA_mc2, Winogrande, GSM8K_flex, CMMLU, and C-EVAL. It's worth noting that our training data did not include any evaluation datasets.

• smollm-tiny-random↗

  yujiepan

• LFM2.5-1.2B-Instruct-Uncensored-GGUF↗

  zaakirio

• NSFW_Master↗

  ZeusAIWorld

• gpt-oss-20b-3MPER0RR-abliterated↗

  3MPER0RR

• CodonTransformer↗

  adibvafa

  **CodonTransformer** is the ultimate tool for codon optimization, transforming protein sequences into optimized DNA sequences specific for your target organisms. Whether you are a researcher or a practitioner in genetic engineering, CodonTransformer provides a comprehensive suite of features to facilitate your work. By leveraging the Transformer architecture and a user-friendly Jupyter notebook, it reduces the complexity of codon optimization, saving you time and effort. Adibvafa Fallahpour 1,2 \*, Vincent Gureghian 3 \*, Guillaume J. Filion 2 ‡, Ariel B. Lindner 3 ‡, Amir Pandi 3 ‡ 1 Vector Institute for Artificial Intelligence, Toronto ON, Canada 2 University of Toronto Scarborough; Department of Biological Science; Scarborough ON, Canada 3 Université Paris Cité, INSERM U1284, Center for Research and Interdisciplinarity, F-75006 Paris, France \* These authors contributed equally to this work. ‡ To whom correspondence should be addressed: guillaume.filion@utoronto.ca, ariel.lindner@inserm.fr, amir.pandi@cri-paris.org

• ai_music_detection_large_60s↗

  AI-Music-Detection

• ChemLLM-7B-Chat-1_5-SFT↗

  AI4Chem