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README.md
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@@ -47,7 +47,7 @@ You are an AI programming assistant, utilizing the Deepseek Coder model, develop
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## Compatibility
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These quantised GGUFv3 files are compatible with llama.cpp from February
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They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp.
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| [OpenCodeInterpreter-DS-6.7B.IQ4_XS.gguf](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.IQ4_XS.gguf) | IQ4_XS | 4 | 3.4 GB| 5.4 GB | small, substantial quality loss |
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Generated importance matrix file: [OpenCodeInterpreter-DS-6.7B.imatrix.dat](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.imatrix.dat)
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Generated importance matrix file (4K context): [OpenCodeInterpreter-DS-6.7B.imatrix-4096.dat](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.imatrix-4096.dat)
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**Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
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If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
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For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
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<!-- README_GGUF.md-how-to-run end -->
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<!-- compatibility_gguf start -->
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## Compatibility
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These quantised GGUFv3 files are compatible with llama.cpp from February 27th 2024 onwards, as of commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307)
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They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp.
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| [OpenCodeInterpreter-DS-6.7B.IQ4_XS.gguf](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.IQ4_XS.gguf) | IQ4_XS | 4 | 3.4 GB| 5.4 GB | small, substantial quality loss |
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Generated importance matrix file: [OpenCodeInterpreter-DS-6.7B.imatrix.dat](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.imatrix.dat)
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Generated importance matrix file (4K context): [OpenCodeInterpreter-DS-6.7B.imatrix-4096.dat](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.imatrix-4096.dat)
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**Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
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If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
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If you are low on V/RAM try quantizing the K-cache with `-ctk q8_0` or even `-ctk q4_0` for big memory savings (depending on context size).
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There is a similar option for V-cache (`-ctv`), however that is [not working yet](https://github.com/ggerganov/llama.cpp/issues/4425).
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For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
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