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