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@@ -75,7 +75,7 @@ cmake --build . --config Release
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#### Option 1: Download pre-quantized MiniGPT4 model
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Pre-quantized models are avaliable on Hugging Face ~ [7B](https://huggingface.co/datasets/maknee/minigpt4-7b-ggml/tree/main) or [13B](https://huggingface.co/datasets/maknee/minigpt4-13b-ggml/tree/main).
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Pre-quantized models are available on Hugging Face ~ [7B](https://huggingface.co/datasets/maknee/minigpt4-7b-ggml/tree/main) or [13B](https://huggingface.co/datasets/maknee/minigpt4-13b-ggml/tree/main).
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Recommended for reliable results, but slow inference speed: [minigpt4-13B-f16.bin](https://huggingface.co/datasets/maknee/minigpt4-13b-ggml/blob/main/minigpt4-13B-f16.bin)
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@@ -129,7 +129,7 @@ python convert.py ~/Downloads/pretrained_minigpt4.pth --outtype f16
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#### Option 1: Download pre-quantized vicuna-v0 model
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Pre-quantized models are avaliable on [Hugging Face](https://huggingface.co/datasets/maknee/ggml-vicuna-v0-quantized/tree/main)
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Pre-quantized models are available on [Hugging Face](https://huggingface.co/datasets/maknee/ggml-vicuna-v0-quantized/tree/main)
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Recommended for reliable results and decent inference speed: [ggml-vicuna-13B-v0-q5_k.bin](https://huggingface.co/datasets/maknee/ggml-vicuna-v0-quantized/blob/main/ggml-vicuna-13B-v0-q5_k.bin)
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