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[Text-Only Weight](https://huggingface.co/CausalLM/miniG/tree/text-only)
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[GGUF (Text-Only, not recommended)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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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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[纯文本权重](https://huggingface.co/CausalLM/miniG/tree/text-only)
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[GGUF (纯文本,不推荐)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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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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[Text-Only Weight](https://huggingface.co/CausalLM/miniG/tree/text-only)
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[GGML with ChatGLM.cpp (recommended)](https://huggingface.co/CausalLM/miniG/tree/ggml): https://github.com/li-plus/chatglm.cpp
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[GGUF (Text-Only, not recommended)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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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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[纯文本权重](https://huggingface.co/CausalLM/miniG/tree/text-only)
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[GGML 用于 ChatGLM.cpp (推荐)](https://huggingface.co/CausalLM/miniG/tree/ggml): https://github.com/li-plus/chatglm.cpp
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[GGUF (纯文本,不推荐)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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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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