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perplexity : fix ETA by warming up the model with an empty run
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@ -752,6 +752,14 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
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params.logit_bias[llama_token_eos(lctx)] = -INFINITY;
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params.logit_bias[llama_token_eos(lctx)] = -INFINITY;
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}
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}
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{
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LOG("warming up the model with an empty run\n");
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const std::vector<llama_token> tmp = { llama_token_bos(lctx), };
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llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads);
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llama_reset_timings(lctx);
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}
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return std::make_tuple(model, lctx);
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return std::make_tuple(model, lctx);
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}
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}
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@ -492,14 +492,6 @@ int main(int argc, char ** argv) {
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std::vector<llama_token> embd;
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std::vector<llama_token> embd;
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std::vector<llama_token> embd_guidance;
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std::vector<llama_token> embd_guidance;
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{
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LOG("warming up the model with an empty run\n");
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const std::vector<llama_token> tmp = { llama_token_bos(ctx), };
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llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
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llama_reset_timings(ctx);
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}
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while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
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while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
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// predict
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// predict
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if (embd.size() > 0) {
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if (embd.size() > 0) {
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