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Update README.md

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@@ -77,7 +77,7 @@ The NT-Java-1.1B GGUFs are supported by llama.cpp and are compatible with a rang
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  | [NT-Java-1.1B_Q5_0.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_0.gguf) | Q5_0 | 5 | 868 MB| 1.08 GB | good quality, prefer Q4_K_M | not recommended, prefer Q4_K_M |
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  | [NT-Java-1.1B_Q5_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_K_M.gguf) | Q5_K_M | 5 | 910 MB| 1.13 GB | excellent quality | recommended, second-best choice |
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  | [NT-Java-1.1B_Q6_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q6_K.gguf) | Q6_K | 6 | 1.02 GB| 1.24 GB | excellent quality | generally not suggested due to size compared to Q5_K_M |
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- | [NT-Java-1.1B_Q8_0.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q8_0.gguf) | Q8_0 | 8 | 1.32 GB| 1.54 GB | top-tier quality, near flawless | usage not recommended with low RAM environments due to large size |
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  **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
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  | [NT-Java-1.1B_Q5_0.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_0.gguf) | Q5_0 | 5 | 868 MB| 1.08 GB | good quality, prefer Q4_K_M | not recommended, prefer Q4_K_M |
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  | [NT-Java-1.1B_Q5_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_K_M.gguf) | Q5_K_M | 5 | 910 MB| 1.13 GB | excellent quality | recommended, second-best choice |
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  | [NT-Java-1.1B_Q6_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q6_K.gguf) | Q6_K | 6 | 1.02 GB| 1.24 GB | excellent quality | generally not suggested due to size compared to Q5_K_M |
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+ | [NT-Java-1.1B_Q8_0.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q8_0.gguf) | Q8_0 | 8 | 1.32 GB| 1.54 GB | top-tier quality, near flawless | preferred in environments with sufficient RAM |
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  **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
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