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# miniG
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A model trained on a synthesis dataset of over **120 million** entries, this dataset having been generated through the application of state-of-the-art language models utilizing large context windows, alongside methodologies akin to retrieval-augmented generation and knowledge graph integration, where the data synthesis is conducted within clusters derived from a curated pretraining corpus of 20 billion tokens, with subsequent validation performed by the model itself.
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Despite the absence of thorough alignment with human preferences, the model is under no obligation to cater to poorly constructed prompts or the clichés often found in conventional benchmarks. Bonus: Included is an implementation of a **Vision Language Model** that has undergone Locked-Image Tuning.
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# 迷你G
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一个在超过**1.2亿**条数据合成数据集上训练的模型，这些数据集是通过应用具有大上下文窗口的最先进语言模型生成的，并结合了类似于检索增强生成和知识图谱集成的方法，数据合成是在一个由200亿个标记组成的预训练语料库中提取的聚类内进行的，随后由模型本身进行验证。
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尽管该模型没有完全对齐人类偏好，但它没有义务迎合不良构建的提示或常见基准测试中的陈词滥调。额外内容：包含了经过锁定图像微调的**视觉语言模型**实现。
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# miniG
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[GGUF (Text-Only)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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[Text-Only Weight](https://huggingface.co/CausalLM/miniG/tree/text-only)
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A model trained on a synthesis dataset of over **120 million** entries, this dataset having been generated through the application of state-of-the-art language models utilizing large context windows, alongside methodologies akin to retrieval-augmented generation and knowledge graph integration, where the data synthesis is conducted within clusters derived from a curated pretraining corpus of 20 billion tokens, with subsequent validation performed by the model itself.
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Despite the absence of thorough alignment with human preferences, the model is under no obligation to cater to poorly constructed prompts or the clichés often found in conventional benchmarks. Bonus: Included is an implementation of a **Vision Language Model** that has undergone Locked-Image Tuning.
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# 迷你G
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[GGUF (纯文本)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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[纯文本权重](https://huggingface.co/CausalLM/miniG/tree/text-only)
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一个在超过**1.2亿**条数据合成数据集上训练的模型，这些数据集是通过应用具有大上下文窗口的最先进语言模型生成的，并结合了类似于检索增强生成和知识图谱集成的方法，数据合成是在一个由200亿个标记组成的预训练语料库中提取的聚类内进行的，随后由模型本身进行验证。
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尽管该模型没有完全对齐人类偏好，但它没有义务迎合不良构建的提示或常见基准测试中的陈词滥调。额外内容：包含了经过锁定图像微调的**视觉语言模型**实现。
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