vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

common : refactor cli arg parsing (#7675)

Browse source 
* common : gpt_params_parse do not print usage

* common : rework usage print (wip)

* common : valign

* common : rework print_usage

* infill : remove cfg support

* common : reorder args

* server : deduplicate parameters

ggml-ci

* common : add missing header

ggml-ci

* common : remote --random-prompt usages

ggml-ci

* examples : migrate to gpt_params

ggml-ci

* batched-bench : migrate to gpt_params

* retrieval : migrate to gpt_params

* common : change defaults for escape and n_ctx

* common : remove chatml and instruct params

ggml-ci

* common : passkey use gpt_params
This commit is contained in:
Georgi Gerganov 2024-06-04 21:23:39 +03:00 • committed by GitHub
parent 554c247caf
commit 1442677f92
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
34 changed files with 899 additions and 1455 deletions
Download patch file Download diff file Expand all files Collapse all files

12
examples/perplexity/perplexity.cpp
View file

@ -1032,7 +1032,7 @@ struct winogrande_entry {
std::vector<llama_token> seq_tokens[2];
};
static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string& prompt) {
static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string & prompt) {
std::vector<winogrande_entry> result;
std::istringstream in(prompt);
std::string line;
@ -1964,12 +1964,14 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
int main(int argc, char ** argv) {
gpt_params params;
params.n_ctx = 512;
params.logits_all = true;
if (!gpt_params_parse(argc, argv, params)) {
gpt_params_print_usage(argc, argv, params);
return 1;
}
params.logits_all = true;
const int32_t n_ctx = params.n_ctx;
if (n_ctx <= 0) {
@ -2006,9 +2008,6 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = string_random_prompt(rng);
}
llama_backend_init();
llama_numa_init(params.numa);
@ -2027,6 +2026,7 @@ int main(int argc, char ** argv) {
}
const int n_ctx_train = llama_n_ctx_train(model);
if (params.n_ctx > n_ctx_train) {
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, params.n_ctx);