Recent comments in /f/MachineLearning

ninjasaid13 t1_jcu9nfv wrote

Reply to comment by simpleuserhere in [Research] Alpaca 7B language model running on my Pixel 7 by simpleuserhere

I believe I already have the build.

I still get this error

C:\Users\****\Downloads\alpaca\alpaca.cpp>make chat
I llama.cpp build info: I UNAME_S:  CYGWIN_NT-10.0 I UNAME_P:  
unknown I UNAME_M:  x86_64 I CFLAGS:   -I.              -O3 -
DNDEBUG -std=c11   -fPIC -mfma -mf16c -mavx -mavx2 I 
CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC I 
LDFLAGS: I CC:       cc (GCC) 10.2.0 I CXX:      g++ (GCC) 
10.2.0
g++ -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC chat.cpp 
ggml.o utils.o -o chat chat.cpp: In function 'int main(int, 
char**)': chat.cpp:883:26: error: aggregate 'main(int, 
char**)::sigaction sigint_action' has incomplete type and 
cannot be defined 883 |         struct sigaction 
sigint_action; |                          ~~~~~~~~~~~~ 
chat.cpp:885:9: error: 'sigemptyset' was not declared in this 
scope 885 |         sigemptyset (&sigint_action.sa_mask); |         
~~~~~~~~~~ chat.cpp:887:47: error: invalid use of incomplete 
type 'struct main(int, char**)::sigaction' 887 |         
sigaction(SIGINT, &sigint_action, NULL); |                                               
^ chat.cpp:883:16: note: forward declaration of 'struct 
main(int, char**)::sigaction' 883 |         struct sigaction 
sigint_action; |                ~~~~~~~~ make: *** [Makefile:195: chat] Error 1
1

ninjasaid13 t1_jcu1odb wrote

Reply to comment by simpleuserhere in [Research] Alpaca 7B language model running on my Pixel 7 by simpleuserhere

I have a problem with

C:\Users\****\source\repos\alpaca.cpp\build>make chat
make: *** No rule to make target 'chat'.  Stop.

and

C:\Users\****\source\repos\alpaca.cpp>make chat
I llama.cpp build info: I UNAME_S:  CYGWIN_NT-10.0 I 
UNAME_P:  unknown I UNAME_M:  x86_64 I CFLAGS:   -I.              
-O3 -DNDEBUG -std=c11   -fPIC -mfma -mf16c -mavx -
mavx2 I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -
std=c++11 -fPIC I LDFLAGS: I CC:       cc (GCC) 
10.2.0 I CXX:      g++ (GCC) 10.2.0
cc  -I.              -O3 -DNDEBUG -std=c11   -fPIC -
mfma -mf16c -mavx -mavx2   -c ggml.c -o ggml.o g++ -
I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -c 
utils.cpp -o utils.o g++ -I. -I./examples -O3 -
DNDEBUG -std=c++11 -fPIC chat.cpp ggml.o utils.o -o 
chat chat.cpp: In function 'int main(int, char**)': 
chat.cpp:883:26: error: aggregate 'main(int, 
char**)::sigaction sigint_action' has incomplete type 
and cannot be defined 883 |         struct sigaction 
sigint_action; |                          
~~~~~~~~~~~~ chat.cpp:885:9: error: 'sigemptyset' was 
not declared in this scope 885 |         sigemptyset 
(&sigint_action.sa_mask); |         ~~~~~~~~~~ 
chat.cpp:887:47: error: invalid use of incomplete 
type 'struct main(int, char**)::sigaction' 887 |         
sigaction(SIGINT, &sigint_action, NULL); |                                               
^ chat.cpp:883:16: note: forward declaration of 
'struct main(int, char**)::sigaction' 883 |         
struct sigaction sigint_action; |                
~~~~~~~~ make: *** [Makefile:195: chat] Error 1

using windows.

1

michaelthwan_ai OP t1_jctvmqe wrote

Reply to comment by [deleted] in [P] searchGPT - a bing-like LLM-based Grounded Search Engine (with Demo, github) by michaelthwan_ai

Cool! Thanks for the sharing.

During my development, I've also found 5+ projects, some open-source and some are closed, where they are doing similar things.

In exact, it is called retrieval-based language model.

Some discussion on that:

https://ai.stanford.edu/blog/retrieval-based-NLP/

11

gamerx88 t1_jctqruk wrote

Reply to [D] Unit and Integration Testing for ML Pipelines by Fender6969

For ETL, write unit tests to handle some input edge cases. E.g Null values, mis-formatting, values out of range as well as some simple working cases.

For model training, the test focus is on having "valid" hyperparams and configurations. I write test cases to try to overfit on a small training set. i.e Confirm the model learns. There are also some robustness tests that I sometimes run post training, but those are very specific to certain NLP tasks, applications.

For model serving, successful parsing of the request and subsequent feature transformation (if any), very similar to ETL.

2