upstream-archive/llama.cpp
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Merge branch 'master' into xsn/decision_clef

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Xuan Son Nguyen
2026-10-02 16:57:29 +02:00
parent 6dd3354ccf 46ca246de9
commit c290b7f463
9 changed files with 125 additions and 8 deletions
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tools/server/server-decision.cpp
+27 -5
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@@ -75,7 +75,7 @@ void server_decision_context::init(const llama_model * model) {
}
n_options_max = labels.size();
noul_true_first = true;
} else if (model_type == COMMON_DECISION_TYPE_LEV) {
} else if (model_type == COMMON_DECISION_TYPE_LEV || model_type == COMMON_DECISION_TYPE_NIMBLE) {
// label codes are A..Z then AA..ZZ, only the ones that are a single token are used
std::vector<std::string> codes;
for (char a = 'A'; a <= 'Z'; a++) {
@@ -363,7 +363,7 @@ size_t server_decision_context::n_outputs(const server_decision_question & quest
return question.options.size();
}
std::string server_decision_context::render(const json & state, const server_decision_question & question, size_t variant, size_t n_images) const {
json server_decision_context::render_options(const server_decision_question & question, size_t variant) const {
const size_t n_options = question.options.size();
// the second variant shows the options in the reverse order
@@ -385,16 +385,37 @@ std::string server_decision_context::render(const json & state, const server_dec
}
options.push_back(option);
}
return options;
}
std::string server_decision_context::render(
const json & state,
const std::vector<server_decision_question> & questions,
const server_decision_question & question,
size_t variant,
size_t n_images) const {
// the template is given raw JSON values, it serializes the ones that are not strings
json inp = json{
{"id", question.id},
{"type", decision_question_type_name(question.type)},
{"instructions", question.instructions},
{"state", state},
{"options", options},
{"options", render_options(question, variant)},
};
// the nimble prompt lists all the questions of the request
if (type == COMMON_DECISION_TYPE_NIMBLE) {
inp["questions"] = json::array();
for (const auto & q : questions) {
inp["questions"].push_back(json{
{"id", q.id},
{"type", decision_question_type_name(q.type)},
{"instructions", q.instructions},
{"options", render_options(q, 0)},
});
}
}
// lev was trained with sorted keys
if (type == COMMON_DECISION_TYPE_LEV) {
inp = decision_sort_keys(inp);
@@ -430,15 +451,16 @@ std::string server_decision_context::render(const json & state, const server_dec
void server_decision_context::fill_task(
const json & state,
const std::vector<server_decision_question> & questions,
const server_decision_question & question,
size_t variant,
const std::vector<raw_buffer> & files,
mtmd_context * mctx,
const mtmd_helper_init_opt & init_opt,
server_task & task) const {
const std::string prompt = render(state, question, variant, files.size());
const std::string prompt = render(state, questions, question, variant, files.size());
if (type == COMMON_DECISION_TYPE_OPENJEV || type == COMMON_DECISION_TYPE_LEV) {
if (type == COMMON_DECISION_TYPE_OPENJEV || type == COMMON_DECISION_TYPE_LEV || type == COMMON_DECISION_TYPE_NIMBLE) {
// lev reads the ratings of a noul question at its first labels, not at the digits
task.decision.labels.assign(labels.begin(), labels.begin() + n_outputs(question));
if (!files.empty()) {