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@@ -145,44 +145,16 @@ python3 -m pip install torch numpy sentencepiece
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python3 convert-pth-to-ggml.py models/7B/ 1
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# quantize the model to 4-bits
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-./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2
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+./quantize.sh 7B
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# run the inference
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./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 128
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```
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-For the bigger models, there are a few extra quantization steps. For example, for LLaMA-13B, converting to FP16 format
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-will create 2 ggml files, instead of one:
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-
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-```bash
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-ggml-model-f16.bin
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-ggml-model-f16.bin.1
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-```
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-
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-You need to quantize each of them separately like this:
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-
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-```bash
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-./quantize ./models/13B/ggml-model-f16.bin ./models/13B/ggml-model-q4_0.bin 2
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-./quantize ./models/13B/ggml-model-f16.bin.1 ./models/13B/ggml-model-q4_0.bin.1 2
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-```
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-
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-Everything else is the same. Simply run:
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-
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-```bash
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-./main -m ./models/13B/ggml-model-q4_0.bin -t 8 -n 128
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-```
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-
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-The number of files generated for each model is as follows:
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-
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-```
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-7B -> 1 file
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-13B -> 2 files
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-30B -> 4 files
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-65B -> 8 files
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-```
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-
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When running the larger models, make sure you have enough disk space to store all the intermediate files.
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+TODO: add model disk/mem requirements
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+
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### Interactive mode
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If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
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