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Merge commit 'df270ef74596da8f1178f08991f4c51f18c9ee82' into concedo_experimental

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# Conflicts:
#	Makefile
#	common/CMakeLists.txt
#	common/common.h
#	common/sampling.cpp
#	common/sampling.h
#	examples/infill/infill.cpp
#	examples/llama-bench/llama-bench.cpp
#	examples/quantize-stats/quantize-stats.cpp
#	examples/server/server.cpp
#	include/llama.h
#	src/llama-sampling.cpp
#	src/llama-sampling.h
#	src/llama.cpp
#	tests/test-grammar-integration.cpp
#	tests/test-grammar-parser.cpp
#	tests/test-json-schema-to-grammar.cpp
#	tests/test-llama-grammar.cpp
#	tests/test-sampling.cpp
This commit is contained in:
Concedo
2024-09-09 17:10:08 +08:00
parent 70cdb55cc9 df270ef745
commit 12fd16bfd4
86 changed files with 3406 additions and 7795 deletions
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examples/embedding/README.md
-60
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@@ -1,60 +0,0 @@
# llama.cpp/example/embedding
This example demonstrates generate high-dimensional embedding vector of a given text with llama.cpp.
## Quick Start
To get started right away, run the following command, making sure to use the correct path for the model you have:
### Unix-based systems (Linux, macOS, etc.):
```bash
./llama-embedding -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>/dev/null
```
### Windows:
```powershell
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.
## extra parameters
### --embd-normalize $integer$
| $integer$ | 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 | $\Large{x_i \over\sqrt[p]{\sum \lvert x_i\rvert^p}}$
### --embd-output-format $'string'$
| $'string'$ | 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 |
### --embd-separator $"string"$
| $"string"$ | |
|--------------|-|
| "\n" | (default)
| "<#embSep#>" | for exemple
| "<#sep#>" | other exemple
## examples
### Unix-based systems (Linux, macOS, etc.):
```bash
./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
```
### Windows:
```powershell
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
```
examples/embedding/embedding.cpp
+4 -8
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@@ -91,13 +91,7 @@ int main(int argc, char ** argv) {
print_build_info();
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
llama_backend_init();
llama_numa_init(params.numa);
@@ -314,8 +308,10 @@ int main(int argc, char ** argv) {
if (notArray) fprintf(stdout, "\n}\n");
}
LOG_TEE("\n");
llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
// clean up
llama_print_timings(ctx);
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);