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AI 모델 85003

• Unicom-Unified-Multimodal-Modeling-via-Compressed-Continuous-Semantic-Representations↗

  腾讯

• Unichat-llama3-Chinese-8B↗

  UnicomLLM

• Llama-3.2-3B-bnb-4bit↗

  unsloth

• whisper-telugu-base↗

  vasista22

• 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