mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
Final touches
This commit is contained in:
parent
775328064e
commit
319cdb3e1f
@ -114,6 +114,5 @@ python3 convert-pth-to-ggml.py models/7B/ 1
|
||||
In general, it seems to work, but I think it fails for unicode character support. Hopefully, someone can help with that
|
||||
- I don't know yet how much the quantization affects the quality of the generated text
|
||||
- Probably the token sampling can be improved
|
||||
- No Windows support
|
||||
- x86 quantization support [not yet ready](https://github.com/ggerganov/ggml/pull/27). Basically, you want to run this on Apple Silicon
|
||||
|
||||
|
||||
|
1
main.cpp
1
main.cpp
@ -728,6 +728,7 @@ int main(int argc, char ** argv) {
|
||||
|
||||
// end of text token
|
||||
if (embd.back() == 2) {
|
||||
printf(" [end of text]\n");
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
0
models/.gitignore
vendored
Normal file
0
models/.gitignore
vendored
Normal file
54
utils.cpp
54
utils.cpp
@ -231,39 +231,39 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
|
||||
}
|
||||
|
||||
std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos) {
|
||||
auto res = gpt_tokenize(vocab, text);
|
||||
|
||||
if (bos) {
|
||||
res.insert(res.begin(), 1); // TODO: replace with vocab.bos
|
||||
}
|
||||
|
||||
//std::vector<gpt_vocab::id> res;
|
||||
//auto res = gpt_tokenize(vocab, text);
|
||||
|
||||
//if (bos) {
|
||||
// res.push_back(1); // TODO: replace with vocab.bos
|
||||
// res.insert(res.begin(), 1); // TODO: replace with vocab.bos
|
||||
//}
|
||||
|
||||
// find the longest token that matches the text
|
||||
//int pos = 0;
|
||||
//while (true) {
|
||||
// int l = 0;
|
||||
// int t = 0;
|
||||
// for (const auto & kv : vocab.id_to_token) {
|
||||
// if (kv.second.size() < l) continue;
|
||||
// if (kv.second.size() > text.size() - pos) continue;
|
||||
// if (text.substr(pos, kv.second.size()) == kv.second) {
|
||||
// l = kv.second.size();
|
||||
// t = kv.first;
|
||||
// }
|
||||
// }
|
||||
std::vector<gpt_vocab::id> res;
|
||||
|
||||
// if (l == 0 && t != 13) {
|
||||
// break;
|
||||
// }
|
||||
if (bos) {
|
||||
res.push_back(1); // TODO: replace with vocab.bos
|
||||
}
|
||||
|
||||
// res.push_back(t);
|
||||
// pos += l;
|
||||
//}
|
||||
//find the longest token that matches the text
|
||||
int pos = 0;
|
||||
while (true) {
|
||||
int l = 0;
|
||||
int t = 0;
|
||||
for (const auto & kv : vocab.id_to_token) {
|
||||
if (kv.second.size() < l) continue;
|
||||
if (kv.second.size() > text.size() - pos) continue;
|
||||
if (text.substr(pos, kv.second.size()) == kv.second) {
|
||||
l = kv.second.size();
|
||||
t = kv.first;
|
||||
}
|
||||
}
|
||||
|
||||
if (l == 0 && t != 13) {
|
||||
break;
|
||||
}
|
||||
|
||||
res.push_back(t);
|
||||
pos += l;
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
|
6
utils.h
6
utils.h
@ -15,12 +15,12 @@
|
||||
struct gpt_params {
|
||||
int32_t seed = -1; // RNG seed
|
||||
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
|
||||
int32_t n_predict = 200; // new tokens to predict
|
||||
int32_t n_predict = 128; // new tokens to predict
|
||||
|
||||
// sampling parameters
|
||||
int32_t top_k = 100;
|
||||
int32_t top_k = 40;
|
||||
float top_p = 0.95f;
|
||||
float temp = 0.8f;
|
||||
float temp = 0.80f;
|
||||
|
||||
int32_t n_batch = 8; // batch size for prompt processing
|
||||
|
||||
|
Loading…
Reference in New Issue
Block a user