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Update README.md
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README.md
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https://github.com/yule-BUAA/MergeLM
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https://github.com/cg123/mergekit/tree/dare'
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24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2. I go into more detail in this [Reddit post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/), and recommend exl2 quantizations on data similar to the desired task, such as these targeted at fiction:
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[4.0bpw](https://huggingface.co/brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties-exl2-4bpw-fiction)
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[3.1bpw](https://huggingface.co/brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties-exl2-3.1bpw-fiction)
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***
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Merged with the following config, and the tokenizer from chargoddard's Yi-Llama:
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https://github.com/yule-BUAA/MergeLM
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https://github.com/cg123/mergekit/tree/dare'
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***
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24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2. I go into more detail in this [Reddit post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/), and recommend exl2 quantizations on data similar to the desired task, such as these targeted at fiction: [4.0bpw](https://huggingface.co/brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties-exl2-4bpw-fiction) [3.1bpw](https://huggingface.co/brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties-exl2-3.1bpw-fiction)
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***
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Merged with the following config, and the tokenizer from chargoddard's Yi-Llama:
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