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61 lines
2.1 KiB
Markdown
61 lines
2.1 KiB
Markdown
# llama.cpp/example/embedding
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This example demonstrates generate high-dimensional embedding vector of a given text with llama.cpp.
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## Quick Start
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To get started right away, run the following command, making sure to use the correct path for the model you have:
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### Unix-based systems (Linux, macOS, etc.):
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```bash
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./llama-embedding -m ./path/to/model --log-disable -p "Hello World!" 2>/dev/null
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```
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### Windows:
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```powershell
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llama-embedding.exe -m ./path/to/model --log-disable -p "Hello World!" 2>$null
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```
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The above command will output space-separated float values.
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## extra parameters
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### --embd-normalize $integer$
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| $integer$ | description | formula |
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|-----------|---------------------|---------|
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| $-1$ | none |
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| $0$ | max absolute int16 | $\Large{{32760 * x_i} \over\max \lvert x_i\rvert}$
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| $1$ | taxicab | $\Large{x_i \over\sum \lvert x_i\rvert}$
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| $2$ | euclidean (default) | $\Large{x_i \over\sqrt{\sum x_i^2}}$
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| $>2$ | p-norm | $\Large{x_i \over\sqrt[p]{\sum \lvert x_i\rvert^p}}$
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### --embd-output-format $'string'$
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| $'string'$ | description | |
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|------------|------------------------------|--|
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| '' | same as before | (default)
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| 'array' | single embeddings | $[[x_1,...,x_n]]$
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| | multiple embeddings | $[[x_1,...,x_n],[x_1,...,x_n],...,[x_1,...,x_n]]$
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| 'json' | openai style |
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| 'json+' | add cosine similarity matrix |
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### --embd-separator $"string"$
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| $"string"$ | |
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|--------------|-|
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| "\n" | (default)
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| "<#embSep#>" | for exemple
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| "<#sep#>" | other exemple
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## examples
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### Unix-based systems (Linux, macOS, etc.):
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```bash
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./embedding -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null
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```
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### Windows:
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```powershell
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embedding.exe -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null
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```
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