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1 files changed, 2005 insertions, 0 deletions

diff --git a/llama.cpp/tools/server/server-task.cpp b/llama.cpp/tools/server/server-task.cpp
new file mode 100644
index 0000000..a137427
--- /dev/null
+++ b/llama.cpp/tools/server/server-task.cpp
@@ -0,0 +1,2005 @@
+#include "server-common.h"
+#include "server-task.h"
+
+#include "common.h"
+#include "llama.h"
+#include "chat.h"
+#include "sampling.h"
+#include "speculative.h"
+#include "json-schema-to-grammar.h"
+
+using json = nlohmann::ordered_json;
+
+//
+// task_params
+//
+
+json task_params::format_logit_bias(const std::vector<llama_logit_bias> & logit_bias) const {
+    json data = json::array();
+    for (const auto & lb : logit_bias) {
+        data.push_back(json{
+            {"bias", lb.bias},
+            {"token", lb.token},
+        });
+    }
+    return data;
+}
+
+json task_params::to_json(bool only_metrics) const {
+    std::vector<std::string> samplers;
+    samplers.reserve(sampling.samplers.size());
+    for (const auto & sampler : sampling.samplers) {
+        samplers.emplace_back(common_sampler_type_to_str(sampler));
+    }
+
+    json lora = json::array();
+    for (auto & it : this->lora) {
+        lora.push_back({{"id", it.first}, {"scale", it.second}});
+    }
+
+    if (only_metrics) {
+        return json {
+            {"seed",                      sampling.seed},
+            {"temperature",               sampling.temp},
+            {"dynatemp_range",            sampling.dynatemp_range},
+            {"dynatemp_exponent",         sampling.dynatemp_exponent},
+            {"top_k",                     sampling.top_k},
+            {"top_p",                     sampling.top_p},
+            {"min_p",                     sampling.min_p},
+            {"top_n_sigma",               sampling.top_n_sigma},
+            {"xtc_probability",           sampling.xtc_probability},
+            {"xtc_threshold",             sampling.xtc_threshold},
+            {"typical_p",                 sampling.typ_p},
+            {"repeat_last_n",             sampling.penalty_last_n},
+            {"repeat_penalty",            sampling.penalty_repeat},
+            {"presence_penalty",          sampling.penalty_present},
+            {"frequency_penalty",         sampling.penalty_freq},
+            {"dry_multiplier",            sampling.dry_multiplier},
+            {"dry_base",                  sampling.dry_base},
+            {"dry_allowed_length",        sampling.dry_allowed_length},
+            {"dry_penalty_last_n",        sampling.dry_penalty_last_n},
+            {"mirostat",                  sampling.mirostat},
+            {"mirostat_tau",              sampling.mirostat_tau},
+            {"mirostat_eta",              sampling.mirostat_eta},
+            {"max_tokens",                n_predict},
+            {"n_predict",                 n_predict}, // TODO: deduplicate?
+            {"n_keep",                    n_keep},
+            {"n_discard",                 n_discard},
+            {"ignore_eos",                sampling.ignore_eos},
+            {"stream",                    stream},
+            {"n_probs",                   sampling.n_probs},
+            {"min_keep",                  sampling.min_keep},
+            {"chat_format",               common_chat_format_name(chat_parser_params.format)},
+            {"reasoning_format",          common_reasoning_format_name(chat_parser_params.reasoning_format)},
+            {"reasoning_in_content",      chat_parser_params.reasoning_in_content},
+            {"thinking_forced_open",      chat_parser_params.thinking_forced_open},
+            {"samplers",                  samplers},
+            {"speculative.n_max",         speculative.n_max},
+            {"speculative.n_min",         speculative.n_min},
+            {"speculative.p_min",         speculative.p_min},
+            {"speculative.type",          common_speculative_type_to_str(speculative.type)},
+            {"speculative.ngram_size_n",  speculative.ngram_size_n},
+            {"speculative.ngram_size_m",  speculative.ngram_size_m},
+            {"speculative.ngram_m_hits",  speculative.ngram_min_hits},
+            {"timings_per_token",         timings_per_token},
+            {"post_sampling_probs",       post_sampling_probs},
+            {"backend_sampling",          sampling.backend_sampling},
+            {"lora",                      lora},
+        };
+    }
+
+    auto grammar_triggers = json::array();
+    for (const auto & trigger : sampling.grammar_triggers) {
+        server_grammar_trigger ct(trigger);
+        grammar_triggers.push_back(ct.to_json());
+    }
+
+    return json {
+        {"seed",                      sampling.seed},
+        {"temperature",               sampling.temp},
+        {"dynatemp_range",            sampling.dynatemp_range},
+        {"dynatemp_exponent",         sampling.dynatemp_exponent},
+        {"top_k",                     sampling.top_k},
+        {"top_p",                     sampling.top_p},
+        {"min_p",                     sampling.min_p},
+        {"top_n_sigma",               sampling.top_n_sigma},
+        {"xtc_probability",           sampling.xtc_probability},
+        {"xtc_threshold",             sampling.xtc_threshold},
+        {"typical_p",                 sampling.typ_p},
+        {"repeat_last_n",             sampling.penalty_last_n},
+        {"repeat_penalty",            sampling.penalty_repeat},
+        {"presence_penalty",          sampling.penalty_present},
+        {"frequency_penalty",         sampling.penalty_freq},
+        {"dry_multiplier",            sampling.dry_multiplier},
+        {"dry_base",                  sampling.dry_base},
+        {"dry_allowed_length",        sampling.dry_allowed_length},
+        {"dry_penalty_last_n",        sampling.dry_penalty_last_n},
+        {"dry_sequence_breakers",     sampling.dry_sequence_breakers},
+        {"mirostat",                  sampling.mirostat},
+        {"mirostat_tau",              sampling.mirostat_tau},
+        {"mirostat_eta",              sampling.mirostat_eta},
+        {"stop",                      antiprompt},
+        {"max_tokens",                n_predict},
+        {"n_predict",                 n_predict}, // TODO: deduplicate?
+        {"n_keep",                    n_keep},
+        {"n_discard",                 n_discard},
+        {"ignore_eos",                sampling.ignore_eos},
+        {"stream",                    stream},
+        {"logit_bias",                format_logit_bias(sampling.logit_bias)},
+        {"n_probs",                   sampling.n_probs},
+        {"min_keep",                  sampling.min_keep},
+        {"grammar",                   sampling.grammar},
+        {"grammar_lazy",              sampling.grammar_lazy},
+        {"grammar_triggers",          grammar_triggers},
+        {"preserved_tokens",          sampling.preserved_tokens},
+        {"chat_format",               common_chat_format_name(chat_parser_params.format)},
+        {"reasoning_format",          common_reasoning_format_name(chat_parser_params.reasoning_format)},
+        {"reasoning_in_content",      chat_parser_params.reasoning_in_content},
+        {"thinking_forced_open",      chat_parser_params.thinking_forced_open},
+        {"samplers",                  samplers},
+        {"speculative.n_max",         speculative.n_max},
+        {"speculative.n_min",         speculative.n_min},
+        {"speculative.p_min",         speculative.p_min},
+        {"speculative.type",          common_speculative_type_to_str(speculative.type)},
+        {"speculative.ngram_size_n",  speculative.ngram_size_n},
+        {"speculative.ngram_size_m",  speculative.ngram_size_m},
+        {"speculative.ngram_m_hits",  speculative.ngram_min_hits},
+        {"timings_per_token",         timings_per_token},
+        {"post_sampling_probs",       post_sampling_probs},
+        {"backend_sampling",          sampling.backend_sampling},
+        {"lora",                      lora},
+    };
+}
+
+//
+// task_result_state
+//
+common_chat_msg task_result_state::update_chat_msg(
+        const std::string & text_added,
+        bool is_partial,
+        std::vector<common_chat_msg_diff> & diffs) {
+    generated_text += text_added;
+    auto msg_prv_copy = chat_msg;
+    SRV_DBG("Parsing chat message: %s\n", generated_text.c_str());
+    auto new_msg = common_chat_parse(
+        generated_text,
+        is_partial,
+        chat_parser_params);
+    if (!new_msg.empty()) {
+        new_msg.set_tool_call_ids(generated_tool_call_ids, gen_tool_call_id);
+        chat_msg = new_msg;
+        diffs = common_chat_msg_diff::compute_diffs(msg_prv_copy, new_msg.empty() ? msg_prv_copy : new_msg);
+    }
+    return chat_msg;
+}
+
+//
+// server_task
+//
+
+task_params server_task::params_from_json_cmpl(
+        const llama_vocab * vocab,
+        const common_params & params_base,
+        const int n_ctx_slot,
+        const json & data) {
+    task_params params;
+
+    // Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
+    task_params defaults;
+    defaults.sampling      = params_base.sampling;
+    defaults.speculative   = params_base.speculative;
+    defaults.n_keep        = params_base.n_keep;
+    defaults.n_predict     = params_base.n_predict;
+    defaults.n_cache_reuse = params_base.n_cache_reuse;
+    defaults.cache_prompt  = params_base.cache_prompt;
+    defaults.antiprompt    = params_base.antiprompt;
+
+    // enabling this will output extra debug information in the HTTP responses from the server
+    params.verbose           = params_base.verbosity > 9;
+    params.timings_per_token = json_value(data, "timings_per_token", false);
+
+    params.stream           = json_value(data,       "stream",             false);
+    auto stream_opt         = json_value(data,       "stream_options",     json::object());
+    params.include_usage    = json_value(stream_opt, "include_usage",      false);
+    params.cache_prompt     = json_value(data,       "cache_prompt",       defaults.cache_prompt);
+    params.return_tokens    = json_value(data,       "return_tokens",      false);
+    params.return_progress  = json_value(data,       "return_progress",    false);
+    params.n_predict        = json_value(data,       "n_predict",          json_value(data, "max_tokens", defaults.n_predict));
+    params.n_indent         = json_value(data,       "n_indent",           defaults.n_indent);
+    params.n_keep           = json_value(data,       "n_keep",             defaults.n_keep);
+    params.n_discard        = json_value(data,       "n_discard",          defaults.n_discard);
+    params.n_cmpl           = json_value(data,       "n_cmpl",             json_value(data, "n", 1));
+    params.n_cache_reuse    = json_value(data,       "n_cache_reuse",      defaults.n_cache_reuse);
+    //params.t_max_prompt_ms  = json_value(data,       "t_max_prompt_ms",    defaults.t_max_prompt_ms); // TODO: implement
+    params.t_max_predict_ms = json_value(data,       "t_max_predict_ms",   defaults.t_max_predict_ms);
+    params.response_fields  = json_value(data,       "response_fields",    std::vector<std::string>());
+
+    params.sampling.top_k              = json_value(data, "top_k",               defaults.sampling.top_k);
+    params.sampling.top_p              = json_value(data, "top_p",               defaults.sampling.top_p);
+    params.sampling.min_p              = json_value(data, "min_p",               defaults.sampling.min_p);
+    params.sampling.top_n_sigma        = json_value(data, "top_n_sigma",         defaults.sampling.top_n_sigma);
+    params.sampling.xtc_probability    = json_value(data, "xtc_probability",     defaults.sampling.xtc_probability);
+    params.sampling.xtc_threshold      = json_value(data, "xtc_threshold",       defaults.sampling.xtc_threshold);
+    params.sampling.typ_p              = json_value(data, "typical_p",           defaults.sampling.typ_p);
+    params.sampling.temp               = json_value(data, "temperature",         defaults.sampling.temp);
+    params.sampling.dynatemp_range     = json_value(data, "dynatemp_range",      defaults.sampling.dynatemp_range);
+    params.sampling.dynatemp_exponent  = json_value(data, "dynatemp_exponent",   defaults.sampling.dynatemp_exponent);
+    params.sampling.penalty_last_n     = json_value(data, "repeat_last_n",       defaults.sampling.penalty_last_n);
+    params.sampling.penalty_repeat     = json_value(data, "repeat_penalty",      defaults.sampling.penalty_repeat);
+    params.sampling.penalty_freq       = json_value(data, "frequency_penalty",   defaults.sampling.penalty_freq);
+    params.sampling.penalty_present    = json_value(data, "presence_penalty",    defaults.sampling.penalty_present);
+    params.sampling.dry_multiplier     = json_value(data, "dry_multiplier",      defaults.sampling.dry_multiplier);
+    params.sampling.dry_base           = json_value(data, "dry_base",            defaults.sampling.dry_base);
+    params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length",  defaults.sampling.dry_allowed_length);
+    params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n",  defaults.sampling.dry_penalty_last_n);
+    params.sampling.mirostat           = json_value(data, "mirostat",            defaults.sampling.mirostat);
+    params.sampling.mirostat_tau       = json_value(data, "mirostat_tau",        defaults.sampling.mirostat_tau);
+    params.sampling.mirostat_eta       = json_value(data, "mirostat_eta",        defaults.sampling.mirostat_eta);
+    params.sampling.adaptive_target    = json_value(data, "adaptive_target",     defaults.sampling.adaptive_target);
+    params.sampling.adaptive_decay     = json_value(data, "adaptive_decay",      defaults.sampling.adaptive_decay);
+    params.sampling.seed               = json_value(data, "seed",                defaults.sampling.seed);
+    params.sampling.n_probs            = json_value(data, "n_probs",             defaults.sampling.n_probs);
+    params.sampling.min_keep           = json_value(data, "min_keep",            defaults.sampling.min_keep);
+    params.sampling.backend_sampling   = json_value(data, "backend_sampling",    defaults.sampling.backend_sampling);
+    params.post_sampling_probs         = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);
+
+    params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
+    params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
+    params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min);
+
+    params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min);
+    params.speculative.n_min = std::max(params.speculative.n_min, 0);
+    params.speculative.n_max = std::max(params.speculative.n_max, 0);
+
+    params.speculative.type = common_speculative_type_from_name(json_value(data, "speculative.type", common_speculative_type_to_str(defaults.speculative.type)));
+
+    params.speculative.ngram_size_n     = json_value(data, "speculative.ngram_size_n", defaults.speculative.ngram_size_n);
+    params.speculative.ngram_size_m     = json_value(data, "speculative.ngram_size_m", defaults.speculative.ngram_size_m);
+    params.speculative.ngram_min_hits   = json_value(data, "speculative.ngram_m_hits", defaults.speculative.ngram_min_hits);
+
+    params.speculative.ngram_size_n     = std::max(std::min(1, (int) params.speculative.ngram_size_n),     1024);
+    params.speculative.ngram_size_m     = std::max(std::min(1, (int) params.speculative.ngram_size_m),     1024);
+    params.speculative.ngram_min_hits   = std::max(std::min(1, (int) params.speculative.ngram_min_hits),   1024);
+
+    // Use OpenAI API logprobs only if n_probs wasn't provided
+    if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){
+        params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs);
+    }
+
+    if (data.contains("lora")) {
+        if (data.at("lora").is_array()) {
+            params.lora = parse_lora_request(data.at("lora"));
+        } else {
+            throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
+        }
+    } else {
+        params.lora = {};
+    }
+
+    // TODO: add more sanity checks for the input parameters
+
+    if (params.sampling.penalty_last_n < -1) {
+        throw std::runtime_error("Error: repeat_last_n must be >= -1");
+    }
+
+    if (params.sampling.dry_penalty_last_n < -1) {
+        throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
+    }
+
+    if (params.sampling.penalty_last_n == -1) {
+        // note: should be the slot's context and not the full context, but it's ok
+        params.sampling.penalty_last_n = n_ctx_slot;
+    }
+
+    if (params.sampling.dry_penalty_last_n == -1) {
+        params.sampling.dry_penalty_last_n = n_ctx_slot;
+    }
+
+    if (params.sampling.dry_base < 1.0f) {
+        params.sampling.dry_base = defaults.sampling.dry_base;
+    }
+
+    // sequence breakers for DRY
+    {
+        // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
+        // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39
+
+        if (data.contains("dry_sequence_breakers")) {
+            params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
+            if (params.sampling.dry_sequence_breakers.empty()) {
+                throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
+            }
+        }
+    }
+
+    // process "json_schema" and "grammar"
+    if (data.contains("json_schema") && !data.contains("grammar")) {
+        try {
+            auto schema                  = json_value(data, "json_schema", json::object());
+            SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
+            params.sampling.grammar      = json_schema_to_grammar(schema);
+            SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
+        } catch (const std::exception & e) {
+            throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
+        }
+    } else {
+        params.sampling.grammar      = json_value(data, "grammar", defaults.sampling.grammar);
+        SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
+        params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
+        SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
+    }
+
+    {
+        auto it = data.find("chat_format");
+        if (it != data.end()) {
+            params.chat_parser_params.format = static_cast<common_chat_format>(it->get<int>());
+            SRV_INF("Chat format: %s\n", common_chat_format_name(params.chat_parser_params.format));
+        } else {
+            params.chat_parser_params.format = defaults.chat_parser_params.format;
+        }
+        common_reasoning_format reasoning_format = params_base.reasoning_format;
+        if (data.contains("reasoning_format")) {
+            reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
+        }
+        params.chat_parser_params.reasoning_format = reasoning_format;
+        params.chat_parser_params.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
+        params.chat_parser_params.thinking_forced_open = json_value(data, "thinking_forced_open", false);
+        params.chat_parser_params.parse_tool_calls = json_value(data, "parse_tool_calls", false);
+        if (data.contains("chat_parser")) {
+            params.chat_parser_params.parser.load(data.at("chat_parser").get<std::string>());
+        }
+    }
+
+    {
+        const auto preserved_tokens = data.find("preserved_tokens");
+        if (preserved_tokens != data.end()) {
+            for (const auto & t : *preserved_tokens) {
+                auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
+                if (ids.size() == 1) {
+                    SRV_DBG("Preserved token: %d\n", ids[0]);
+                    params.sampling.preserved_tokens.insert(ids[0]);
+                } else {
+                    // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
+                    SRV_DBG("Not preserved because more than 1 token: %s\n", t.get<std::string>().c_str());
+                }
+            }
+        }
+        const auto grammar_triggers = data.find("grammar_triggers");
+        if (grammar_triggers != data.end()) {
+            for (const auto & t : *grammar_triggers) {
+                server_grammar_trigger ct(t);
+                if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
+                    const auto & word = ct.value.value;
+                    auto ids = common_tokenize(vocab, word, /* add_special= */ false, /* parse_special= */ true);
+                    if (ids.size() == 1) {
+                        auto token = ids[0];
+                        if (std::find(params.sampling.preserved_tokens.begin(), params.sampling.preserved_tokens.end(), (llama_token) token) == params.sampling.preserved_tokens.end()) {
+                            throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
+                        }
+                        SRV_DBG("Grammar trigger token: %d (`%s`)\n", token, word.c_str());
+                        common_grammar_trigger trigger;
+                        trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
+                        trigger.value = word;
+                        trigger.token = token;
+                        params.sampling.grammar_triggers.push_back(std::move(trigger));
+                    } else {
+                        SRV_DBG("Grammar trigger word: `%s`\n", word.c_str());
+                        params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
+                    }
+                } else {
+                    if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) {
+                        SRV_DBG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str());
+                    } else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) {
+                        SRV_DBG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str());
+                    } else {
+                        throw std::runtime_error("Unknown grammar trigger type");
+                    }
+                    params.sampling.grammar_triggers.emplace_back(std::move(ct.value));
+                }
+            }
+        }
+        if (params.sampling.grammar_lazy && params.sampling.grammar_triggers.empty()) {
+            throw std::runtime_error("Error: no triggers set for lazy grammar!");
+        }
+    }
+
+    {
+        params.sampling.logit_bias.clear();
+
+        const auto & logit_bias = data.find("logit_bias");
+        if (logit_bias != data.end() && logit_bias->is_array()) {
+            const int n_vocab = llama_vocab_n_tokens(vocab);
+            for (const auto & el : *logit_bias) {
+                // TODO: we may want to throw errors here, in case "el" is incorrect
+                if (el.is_array() && el.size() == 2) {
+                    float bias;
+                    if (el[1].is_number()) {
+                        bias = el[1].get<float>();
+                    } else if (el[1].is_boolean() && !el[1].get<bool>()) {
+                        bias = -INFINITY;
+                    } else {
+                        continue;
+                    }
+
+                    if (el[0].is_number_integer()) {
+                        llama_token tok = el[0].get<llama_token>();
+                        if (tok >= 0 && tok < n_vocab) {
+                            params.sampling.logit_bias.push_back({tok, bias});
+                        }
+                    } else if (el[0].is_string()) {
+                        auto toks = common_tokenize(vocab, el[0].get<std::string>(), false);
+                        for (auto tok : toks) {
+                            params.sampling.logit_bias.push_back({tok, bias});
+                        }
+                    }
+                }
+            }
+        } else if (logit_bias != data.end() && logit_bias->is_object()) {
+            const int n_vocab = llama_vocab_n_tokens(vocab);
+            for (const auto & el : logit_bias->items()) {
+                float bias;
+                const auto & key = el.key();
+                const auto & value = el.value();
+                if (value.is_number()) {
+                    bias = value.get<float>();
+                } else if (value.is_boolean() && !value.get<bool>()) {
+                    bias = -INFINITY;
+                } else {
+                    continue;
+                }
+
+                char *end;
+                llama_token tok = strtol(key.c_str(), &end, 10);
+                if (*end == 0) {
+                    if (tok >= 0 && tok < n_vocab) {
+                        params.sampling.logit_bias.push_back({tok, bias});
+                    }
+                } else {
+                    auto toks = common_tokenize(vocab, key, false);
+                    for (auto tok : toks) {
+                        params.sampling.logit_bias.push_back({tok, bias});
+                    }
+                }
+            }
+        }
+
+        params.sampling.ignore_eos = json_value(data, "ignore_eos", params_base.sampling.ignore_eos);
+        if (params.sampling.ignore_eos) {
+            params.sampling.logit_bias.insert(
+                    params.sampling.logit_bias.end(),
+                    defaults.sampling.logit_bias_eog.begin(), defaults.sampling.logit_bias_eog.end());
+        }
+    }
+
+    {
+        params.antiprompt.clear();
+
+        const auto & stop = data.find("stop");
+        if (stop != data.end() && stop->is_array()) {
+            for (const auto & word : *stop) {
+                if (!word.empty()) {
+                    params.antiprompt.push_back(word);
+                }
+            }
+        }
+        // set reverse prompt from cli args if not set in the request
+        if (params.antiprompt.empty()) {
+            params.antiprompt = defaults.antiprompt;
+        }
+    }
+
+    {
+        const auto samplers = data.find("samplers");
+        if (samplers != data.end()) {
+            if (samplers->is_array()) {
+                params.sampling.samplers = common_sampler_types_from_names(*samplers, false);
+            } else if (samplers->is_string()){
+                params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
+            }
+        } else {
+            params.sampling.samplers = defaults.sampling.samplers;
+        }
+    }
+
+    if (params.n_cmpl > params_base.n_parallel) {
+        throw std::runtime_error("n_cmpl cannot be greater than the number of slots, please increase -np");
+    }
+
+    return params;
+}
+
+//
+// result_timings
+//
+
+json result_timings::to_json() const {
+    json base = {
+        {"cache_n",                cache_n},
+
+        {"prompt_n",               prompt_n},
+        {"prompt_ms",              prompt_ms},
+        {"prompt_per_token_ms",    prompt_per_token_ms},
+        {"prompt_per_second",      prompt_per_second},
+
+        {"predicted_n",            predicted_n},
+        {"predicted_ms",           predicted_ms},
+        {"predicted_per_token_ms", predicted_per_token_ms},
+        {"predicted_per_second",   predicted_per_second},
+    };
+
+    if (draft_n > 0) {
+        base["draft_n"] = draft_n;
+        base["draft_n_accepted"] = draft_n_accepted;
+    }
+
+    return base;
+}
+
+//
+// result_prompt_progress
+//
+json result_prompt_progress::to_json() const {
+    return json {
+        {"total",     total},
+        {"cache",     cache},
+        {"processed", processed},
+        {"time_ms",   time_ms},
+    };
+}
+
+static inline std::string stop_type_to_str(stop_type type) {
+    switch (type) {
+        case STOP_TYPE_EOS:   return "eos";
+        case STOP_TYPE_WORD:  return "word";
+        case STOP_TYPE_LIMIT: return "limit";
+        default:              return "none";
+    }
+}
+
+//
+// completion_token_output
+//
+
+json completion_token_output::to_json(bool post_sampling_probs) const {
+    json probs_for_token = json::array();
+    for (const auto & p : probs) {
+        std::string txt(p.txt);
+        txt.resize(validate_utf8(txt));
+        probs_for_token.push_back(json {
+            {"id",      p.tok},
+            {"token",   txt},
+            {"bytes",   str_to_bytes(p.txt)},
+            {
+                post_sampling_probs ? "prob" : "logprob",
+                post_sampling_probs ? p.prob : logarithm(p.prob)
+            },
+        });
+    }
+    return probs_for_token;
+}
+
+json completion_token_output::probs_vector_to_json(const std::vector<completion_token_output> & probs, bool post_sampling_probs) {
+    json out = json::array();
+    for (const auto & p : probs) {
+        std::string txt(p.text_to_send);
+        txt.resize(validate_utf8(txt));
+        out.push_back(json {
+            {"id",           p.tok},
+            {"token",        txt},
+            {"bytes",        str_to_bytes(p.text_to_send)},
+            {
+                post_sampling_probs ? "prob" : "logprob",
+                post_sampling_probs ? p.prob : logarithm(p.prob)
+            },
+            {
+                post_sampling_probs ? "top_probs" : "top_logprobs",
+                p.to_json(post_sampling_probs)
+            },
+        });
+    }
+    return out;
+}
+
+float completion_token_output::logarithm(float x) {
+    // nlohmann::json converts -inf to null, so we need to prevent that
+    return x == 0.0f ? std::numeric_limits<float>::lowest() : std::log(x);
+}
+
+std::vector<unsigned char> completion_token_output::str_to_bytes(const std::string & str) {
+    std::vector<unsigned char> bytes;
+    for (unsigned char c : str) {
+        bytes.push_back(c);
+    }
+    return bytes;
+}
+
+//
+// server_task_result_cmpl_final
+//
+json server_task_result_cmpl_final::to_json() {
+    GGML_ASSERT(is_updated && "update() must be called before to_json()");
+    switch (res_type) {
+        case TASK_RESPONSE_TYPE_NONE:
+            return to_json_non_oaicompat();
+        case TASK_RESPONSE_TYPE_OAI_CMPL:
+            return to_json_oaicompat();
+        case TASK_RESPONSE_TYPE_OAI_CHAT:
+            return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat();
+        case TASK_RESPONSE_TYPE_OAI_RESP:
+            return stream ? to_json_oaicompat_resp_stream() : to_json_oaicompat_resp();
+        case TASK_RESPONSE_TYPE_ANTHROPIC:
+            return stream ? to_json_anthropic_stream() : to_json_anthropic();
+        default:
+            GGML_ASSERT(false && "Invalid task_response_type");
+    }
+}
+
+json server_task_result_cmpl_final::to_json_non_oaicompat() {
+    json res = json {
+        {"index",               index},
+        {"content",             content},
+        {"tokens",              tokens},
+        {"id_slot",             id_slot},
+        {"stop",                true},
+        {"model",               oaicompat_model},
+        {"tokens_predicted",    n_decoded},
+        {"tokens_evaluated",    n_prompt_tokens},
+        {"generation_settings", generation_params.to_json()},
+        {"prompt",              prompt},
+        {"has_new_line",        has_new_line},
+        {"truncated",           truncated},
+        {"stop_type",           stop_type_to_str(stop)},
+        {"stopping_word",       stopping_word},
+        {"tokens_cached",       n_tokens_cached},
+        {"timings",             timings.to_json()},
+    };
+    if (!stream && !probs_output.empty()) {
+        res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
+    }
+    return response_fields.empty() ? res : json_get_nested_values(response_fields, res);
+}
+
+json server_task_result_cmpl_final::to_json_oaicompat() {
+    std::time_t t = std::time(0);
+    json logprobs = json(nullptr); // OAI default to null
+    if (!stream && probs_output.size() > 0) {
+        logprobs = json{
+            {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
+        };
+    }
+    json finish_reason = "length";
+    if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
+        finish_reason = "stop";
+    }
+    json res = json {
+        {"choices",            json::array({
+            json{
+                {"text",          content},
+                {"index",         index},
+                {"logprobs",      logprobs},
+                {"finish_reason", finish_reason},
+            }
+        })},
+        {"created",            t},
+        {"model",              oaicompat_model},
+        {"system_fingerprint", build_info},
+        {"object",             "text_completion"},
+        {"usage", json {
+            {"completion_tokens", n_decoded},
+            {"prompt_tokens",     n_prompt_tokens},
+            {"total_tokens",      n_decoded + n_prompt_tokens}
+        }},
+        {"id", oaicompat_cmpl_id}
+    };
+
+    // extra fields for debugging purposes
+    if (verbose) {
+        res["__verbose"] = to_json_non_oaicompat();
+    }
+    if (timings.prompt_n >= 0) {
+        res.push_back({"timings", timings.to_json()});
+    }
+
+    return res;
+}
+
+json server_task_result_cmpl_final::to_json_oaicompat_chat() {
+    std::string finish_reason = "length";
+    common_chat_msg msg;
+    if (!oaicompat_msg.empty()) {
+        msg = oaicompat_msg;
+    } else {
+        msg.role = "assistant";
+        msg.content = content;
+    }
+    if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
+        finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
+    }
+
+    json choice {
+        {"finish_reason", finish_reason},
+        {"index", index},
+        {"message", msg.to_json_oaicompat()},
+    };
+
+    if (!stream && probs_output.size() > 0) {
+        choice["logprobs"] = json{
+            {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
+        };
+    }
+
+    std::time_t t = std::time(0);
+
+    json res = json {
+        {"choices",            json::array({choice})},
+        {"created",            t},
+        {"model",              oaicompat_model},
+        {"system_fingerprint", build_info},
+        {"object",             "chat.completion"},
+        {"usage", json {
+            {"completion_tokens", n_decoded},
+            {"prompt_tokens",     n_prompt_tokens},
+            {"total_tokens",      n_decoded + n_prompt_tokens}
+        }},
+        {"id", oaicompat_cmpl_id}
+    };
+
+    // extra fields for debugging purposes
+    if (verbose) {
+        res["__verbose"] = to_json_non_oaicompat();
+    }
+    if (timings.prompt_n >= 0) {
+        res.push_back({"timings", timings.to_json()});
+    }
+
+    return res;
+}
+
+json server_task_result_cmpl_final::to_json_oaicompat_chat_stream() {
+    std::time_t t = std::time(0);
+    std::string finish_reason = "length";
+    if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
+        finish_reason = oaicompat_msg.tool_calls.empty() ? "stop" : "tool_calls";
+    }
+
+    json deltas = json::array();
+    for (const auto & diff : oaicompat_msg_diffs) {
+        deltas.push_back({
+            {"choices", json::array({
+                json {
+                    {"finish_reason", nullptr},
+                    {"index", 0},
+                    {"delta", common_chat_msg_diff_to_json_oaicompat(diff)},
+                },
+            })},
+            {"created", t},
+            {"id", oaicompat_cmpl_id},
+            {"model", oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object", "chat.completion.chunk"},
+        });
+    }
+
+    deltas.push_back({
+        {"choices", json::array({
+            json {
+                {"finish_reason", finish_reason},
+                {"index", 0},
+                {"delta", json::object()},
+            },
+        })},
+        {"created",            t},
+        {"id",                 oaicompat_cmpl_id},
+        {"model",              oaicompat_model},
+        {"system_fingerprint", build_info},
+        {"object",             "chat.completion.chunk"},
+    });
+
+    if (include_usage) {
+        // OpenAI API spec for chat.completion.chunks specifies an empty `choices` array for the last chunk when including usage
+        // https://platform.openai.com/docs/api-reference/chat_streaming/streaming#chat_streaming/streaming-choices
+        deltas.push_back({
+            {"choices", json::array()},
+            {"created",            t},
+            {"id",                 oaicompat_cmpl_id},
+            {"model",              oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object",             "chat.completion.chunk"},
+            {"usage", json {
+                {"completion_tokens", n_decoded},
+                {"prompt_tokens",     n_prompt_tokens},
+                {"total_tokens",      n_decoded + n_prompt_tokens},
+            }},
+        });
+    }
+
+    if (timings.prompt_n >= 0) {
+        deltas.back().push_back({"timings", timings.to_json()});
+    }
+
+    // extra fields for debugging purposes
+    if (verbose && !deltas.empty()) {
+        deltas.front()["__verbose"] = to_json_non_oaicompat();
+    }
+
+    return deltas;
+}
+
+json server_task_result_cmpl_final::to_json_oaicompat_resp() {
+    common_chat_msg msg;
+    if (!oaicompat_msg.empty()) {
+        msg = oaicompat_msg;
+    } else {
+        msg.role = "assistant";
+        msg.content = content;
+    }
+
+    std::vector<json> output;
+
+    if (msg.reasoning_content != "") {
+        output.push_back(json {
+            {"id",      "rs_" + random_string()},
+            {"summary", json::array()},
+            {"type",    "reasoning"},
+            {"content", json::array({ json {
+                {"text", msg.reasoning_content},
+                {"type", "reasoning_text"},
+            }})},
+            {"encrypted_content", ""},
+            {"status",            "completed"},
+        });
+    }
+
+    if (msg.content != "") {
+        output.push_back(json {
+            {"content", json::array({ json {
+                {"type",        "output_text"},
+                {"annotations", json::array()},
+                {"logprobs",    json::array()},
+                {"text",        msg.content},
+            }})},
+            {"id",     "msg_" + random_string()},
+            {"role",   msg.role},
+            {"status", "completed"},
+            {"type",   "message"},
+        });
+    }
+
+    for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
+        output.push_back(json {
+            {"type",      "function_call"},
+            {"status",    "completed"},
+            {"arguments", tool_call.arguments},
+            {"call_id",   "fc_" + tool_call.id},
+            {"name",      tool_call.name},
+        });
+    }
+
+    std::time_t t = std::time(0);
+    json res = {
+        {"completed_at", t},
+        {"created_at",   t},
+        {"id",           oai_resp_id},
+        {"model",        oaicompat_model},
+        {"object",       "response"},
+        {"output",       output},
+        {"status",       "completed"},
+        {"usage",        json {
+            {"input_tokens",  n_prompt_tokens},
+            {"output_tokens", n_decoded},
+            {"total_tokens",  n_decoded + n_prompt_tokens},
+        }},
+    };
+
+    return res;
+}
+
+json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() {
+    std::vector<json> server_sent_events;
+    std::vector<json> output;
+
+    if (oaicompat_msg.reasoning_content != "") {
+        const json output_item = json {
+            {"id",      oai_resp_reasoning_id},
+            {"summary", json::array()},
+            {"type",    "reasoning"},
+            {"content", json::array({ json {
+                {"text", oaicompat_msg.reasoning_content},
+                {"type", "reasoning_text"},
+            }})},
+            {"encrypted_content", ""},
+        };
+
+        server_sent_events.push_back(json {
+            {"event", "response.output_item.done"},
+            {"data", json {
+                {"type", "response.output_item.done"},
+                {"item", output_item}
+            }}
+        });
+        output.push_back(output_item);
+    }
+
+    if (oaicompat_msg.content != "") {
+        server_sent_events.push_back(json {
+            {"event", "response.output_text.done"},
+            {"data", json {
+                {"type",    "response.output_text.done"},
+                {"item_id", oai_resp_message_id},
+                {"text",    oaicompat_msg.content}
+            }}
+        });
+
+        const json content_part = {
+            {"type",        "output_text"},
+            {"annotations", json::array()},
+            {"logprobs",    json::array()},
+            {"text",        oaicompat_msg.content}
+        };
+
+        server_sent_events.push_back(json {
+            {"event", "response.content_part.done"},
+            {"data", json {
+                {"type",    "response.content_part.done"},
+                {"item_id", oai_resp_message_id},
+                {"part",    content_part}
+            }}
+        });
+        const json output_item = {
+            {"type",    "message"},
+            {"status",  "completed"},
+            {"id",      oai_resp_message_id},
+            {"content", json::array({content_part})},
+            {"role",    "assistant"}
+        };
+
+        server_sent_events.push_back(json {
+            {"event", "response.output_item.done"},
+            {"data", json {
+                {"type", "response.output_item.done"},
+                {"item", output_item}
+            }}
+        });
+        output.push_back(output_item);
+    }
+
+    for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
+        const json output_item = {
+            {"type",      "function_call"},
+            {"status",    "completed"},
+            {"arguments", tool_call.arguments},
+            {"call_id",   "fc_" + tool_call.id},
+            {"name",      tool_call.name}
+        };
+        server_sent_events.push_back(json {
+            {"event", "response.output_item.done"},
+            {"data", json {
+                {"type", "response.output_item.done"},
+                {"item", output_item}
+            }}
+        });
+        output.push_back(output_item);
+    }
+
+    std::time_t t = std::time(0);
+    server_sent_events.push_back(json {
+        {"event", "response.completed"},
+        {"data", json {
+            {"type", "response.completed"},
+            {"response", json {
+                {"id",         oai_resp_id},
+                {"object",     "response"},
+                {"created_at", t},
+                {"status",     "completed"},
+                {"model",      oaicompat_model},
+                {"output",     output},
+                {"usage",      json {
+                    {"input_tokens",  n_prompt_tokens},
+                    {"output_tokens", n_decoded},
+                    {"total_tokens",  n_decoded + n_prompt_tokens}
+                }}
+            }},
+        }}
+    });
+
+    return server_sent_events;
+}
+
+json server_task_result_cmpl_final::to_json_anthropic() {
+    std::string stop_reason = "max_tokens";
+    if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
+        stop_reason = oaicompat_msg.tool_calls.empty() ? "end_turn" : "tool_use";
+    }
+
+    json content_blocks = json::array();
+
+    common_chat_msg msg;
+    if (!oaicompat_msg.empty()) {
+        msg = oaicompat_msg;
+    } else {
+        msg.role = "assistant";
+        msg.content = content;
+    }
+
+    // thinking block comes first (Anthropic extended thinking format)
+    if (!msg.reasoning_content.empty()) {
+        content_blocks.push_back({
+            {"type", "thinking"},
+            {"thinking", msg.reasoning_content},
+            {"signature", ""}  // empty signature for local models (no cryptographic verification)
+        });
+    }
+
+    if (!msg.content.empty()) {
+        content_blocks.push_back({
+            {"type", "text"},
+            {"text", msg.content}
+        });
+    }
+
+    for (const auto & tool_call : msg.tool_calls) {
+        json tool_use_block = {
+            {"type", "tool_use"},
+            {"id", tool_call.id},
+            {"name", tool_call.name}
+        };
+
+        try {
+            tool_use_block["input"] = json::parse(tool_call.arguments);
+        } catch (const std::exception &) {
+            tool_use_block["input"] = json::object();
+        }
+
+        content_blocks.push_back(tool_use_block);
+    }
+
+    json res = {
+        {"id", oaicompat_cmpl_id},
+        {"type", "message"},
+        {"role", "assistant"},
+        {"content", content_blocks},
+        {"model", oaicompat_model},
+        {"stop_reason", stop_reason},
+        {"stop_sequence", stopping_word.empty() ? nullptr : json(stopping_word)},
+        {"usage", {
+            {"input_tokens", n_prompt_tokens},
+            {"output_tokens", n_decoded}
+        }}
+    };
+
+    return res;
+}
+
+json server_task_result_cmpl_final::to_json_anthropic_stream() {
+    json events = json::array();
+
+    std::string stop_reason = "max_tokens";
+    if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
+        stop_reason = oaicompat_msg.tool_calls.empty() ? "end_turn" : "tool_use";
+    }
+
+    bool has_thinking = !oaicompat_msg.reasoning_content.empty();
+    bool has_text     = !oaicompat_msg.content.empty();
+    size_t num_tool_calls = oaicompat_msg.tool_calls.size();
+
+    // content block indices: thinking (0) -> text (0 or 1) -> tool_use (n+)
+    size_t thinking_block_index = 0;
+    size_t text_block_index     = has_thinking ? 1 : 0;
+
+    bool thinking_block_started = false;
+    bool text_block_started     = false;
+    std::unordered_set<size_t> tool_calls_started;
+
+    for (const auto & diff : oaicompat_msg_diffs) {
+        // handle thinking/reasoning content
+        if (!diff.reasoning_content_delta.empty()) {
+            if (!thinking_block_started) {
+                events.push_back({
+                    {"event", "content_block_start"},
+                    {"data", {
+                        {"type", "content_block_start"},
+                        {"index", thinking_block_index},
+                        {"content_block", {
+                            {"type", "thinking"},
+                            {"thinking", ""}
+                        }}
+                    }}
+                });
+                thinking_block_started = true;
+            }
+
+            events.push_back({
+                {"event", "content_block_delta"},
+                {"data", {
+                    {"type", "content_block_delta"},
+                    {"index", thinking_block_index},
+                    {"delta", {
+                        {"type", "thinking_delta"},
+                        {"thinking", diff.reasoning_content_delta}
+                    }}
+                }}
+            });
+        }
+
+        // handle regular text content
+        if (!diff.content_delta.empty()) {
+            if (!text_block_started) {
+                events.push_back({
+                    {"event", "content_block_start"},
+                    {"data", {
+                        {"type", "content_block_start"},
+                        {"index", text_block_index},
+                        {"content_block", {
+                            {"type", "text"},
+                            {"text", ""}
+                        }}
+                    }}
+                });
+                text_block_started = true;
+            }
+
+            events.push_back({
+                {"event", "content_block_delta"},
+                {"data", {
+                    {"type", "content_block_delta"},
+                    {"index", text_block_index},
+                    {"delta", {
+                        {"type", "text_delta"},
+                        {"text", diff.content_delta}
+                    }}
+                }}
+            });
+        }
+
+        // handle tool calls
+        if (diff.tool_call_index != std::string::npos) {
+            size_t content_block_index = (has_thinking ? 1 : 0) + (has_text ? 1 : 0) + diff.tool_call_index;
+
+            if (tool_calls_started.find(diff.tool_call_index) == tool_calls_started.end()) {
+                const auto & full_tool_call = oaicompat_msg.tool_calls[diff.tool_call_index];
+
+                events.push_back({
+                    {"event", "content_block_start"},
+                    {"data", {
+                        {"type", "content_block_start"},
+                        {"index", content_block_index},
+                        {"content_block", {
+                            {"type", "tool_use"},
+                            {"id", full_tool_call.id},
+                            {"name", full_tool_call.name}
+                        }}
+                    }}
+                });
+                tool_calls_started.insert(diff.tool_call_index);
+            }
+
+            if (!diff.tool_call_delta.arguments.empty()) {
+                events.push_back({
+                    {"event", "content_block_delta"},
+                    {"data", {
+                        {"type", "content_block_delta"},
+                        {"index", content_block_index},
+                        {"delta", {
+                            {"type", "input_json_delta"},
+                            {"partial_json", diff.tool_call_delta.arguments}
+                        }}
+                    }}
+                });
+            }
+        }
+    }
+
+    // close content blocks in order
+    if (has_thinking) {
+        // Anthropic API requires a signature_delta before closing thinking blocks
+        // We use an empty signature since we can't generate a cryptographic signature for local models
+        events.push_back({
+            {"event", "content_block_delta"},
+            {"data", {
+                {"type", "content_block_delta"},
+                {"index", thinking_block_index},
+                {"delta", {
+                    {"type", "signature_delta"},
+                    {"signature", ""}
+                }}
+            }}
+        });
+        events.push_back({
+            {"event", "content_block_stop"},
+            {"data", {
+                {"type", "content_block_stop"},
+                {"index", thinking_block_index}
+            }}
+        });
+    }
+
+    if (has_text) {
+        events.push_back({
+            {"event", "content_block_stop"},
+            {"data", {
+                {"type", "content_block_stop"},
+                {"index", text_block_index}
+            }}
+        });
+    }
+
+    for (size_t i = 0; i < num_tool_calls; i++) {
+        size_t content_block_index = (has_thinking ? 1 : 0) + (has_text ? 1 : 0) + i;
+        events.push_back({
+            {"event", "content_block_stop"},
+            {"data", {
+                {"type", "content_block_stop"},
+                {"index", content_block_index}
+            }}
+        });
+    }
+
+    events.push_back({
+        {"event", "message_delta"},
+        {"data", {
+            {"type", "message_delta"},
+            {"delta", {
+                {"stop_reason", stop_reason},
+                {"stop_sequence", stopping_word.empty() ? nullptr : json(stopping_word)}
+            }},
+            {"usage", {
+                {"output_tokens", n_decoded}
+            }}
+        }}
+    });
+
+    events.push_back({
+        {"event", "message_stop"},
+        {"data", {
+            {"type", "message_stop"}
+        }}
+    });
+
+    return events;
+}
+
+//
+// server_task_result_cmpl_partial
+//
+void server_task_result_cmpl_partial::update(task_result_state & state) {
+    is_updated = true;
+    state.update_chat_msg(content, true, oaicompat_msg_diffs);
+
+    // Copy current state for use in to_json_*() (reflects state BEFORE this chunk)
+    thinking_block_started = state.thinking_block_started;
+    text_block_started     = state.text_block_started;
+
+    oai_resp_id            = state.oai_resp_id;
+    oai_resp_reasoning_id  = state.oai_resp_reasoning_id;
+    oai_resp_message_id    = state.oai_resp_message_id;
+    oai_resp_fc_id         = state.oai_resp_fc_id;
+
+    // track if the accumulated message has any reasoning content
+    anthropic_has_reasoning = !state.chat_msg.reasoning_content.empty();
+
+    // Pre-compute state updates based on diffs (for next chunk)
+    for (const common_chat_msg_diff & diff : oaicompat_msg_diffs) {
+        if (!diff.reasoning_content_delta.empty() && !state.thinking_block_started) {
+            state.thinking_block_started = true;
+        }
+        if (!diff.content_delta.empty() && !state.text_block_started) {
+            state.text_block_started = true;
+        }
+        if (!diff.tool_call_delta.name.empty()) {
+            state.oai_resp_fc_id = diff.tool_call_delta.id;
+        }
+    }
+}
+
+json server_task_result_cmpl_partial::to_json() {
+    GGML_ASSERT(is_updated && "update() must be called before to_json()");
+    switch (res_type) {
+        case TASK_RESPONSE_TYPE_NONE:
+            return to_json_non_oaicompat();
+        case TASK_RESPONSE_TYPE_OAI_CMPL:
+            return to_json_oaicompat();
+        case TASK_RESPONSE_TYPE_OAI_CHAT:
+            return to_json_oaicompat_chat();
+        case TASK_RESPONSE_TYPE_OAI_RESP:
+            return to_json_oaicompat_resp();
+        case TASK_RESPONSE_TYPE_ANTHROPIC:
+            return to_json_anthropic();
+        default:
+            GGML_ASSERT(false && "Invalid task_response_type");
+    }
+}
+
+json server_task_result_cmpl_partial::to_json_non_oaicompat() {
+    // non-OAI-compat JSON
+    json res = json {
+        {"index",            index},
+        {"content",          content},
+        {"tokens",           tokens},
+        {"stop",             false},
+        {"id_slot",          id_slot},
+        {"tokens_predicted", n_decoded},
+        {"tokens_evaluated", n_prompt_tokens},
+    };
+    // populate the timings object when needed (usually for the last response or with timings_per_token enabled)
+    if (timings.prompt_n > 0) {
+        res.push_back({"timings", timings.to_json()});
+    }
+    if (is_progress) {
+        res.push_back({"prompt_progress", progress.to_json()});
+    }
+    if (!prob_output.probs.empty()) {
+        res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs);
+    }
+    return res;
+}
+
+json server_task_result_cmpl_partial::to_json_oaicompat() {
+    std::time_t t = std::time(0);
+    json logprobs = json(nullptr); // OAI default to null
+    if (prob_output.probs.size() > 0) {
+        logprobs = json{
+            {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
+        };
+    }
+    json res = json {
+        {"choices",            json::array({
+            json{
+                {"text",          content},
+                {"index",         index},
+                {"logprobs",      logprobs},
+                {"finish_reason", nullptr},
+            }
+        })},
+        {"created",            t},
+        {"model",              oaicompat_model},
+        {"system_fingerprint", build_info},
+        {"object",             "text_completion"},
+        {"id",                 oaicompat_cmpl_id}
+    };
+
+    // extra fields for debugging purposes
+    if (verbose) {
+        res["__verbose"] = to_json_non_oaicompat();
+    }
+    if (timings.prompt_n >= 0) {
+        res.push_back({"timings", timings.to_json()});
+    }
+    if (is_progress) {
+        res.push_back({"prompt_progress", progress.to_json()});
+    }
+
+    return res;
+}
+
+json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
+    bool first = n_decoded == 1;
+    std::time_t t = std::time(0);
+    json choices;
+
+    std::vector<json> deltas;
+    auto add_delta = [&](const json & delta) {
+        deltas.push_back({
+            {"choices", json::array({
+                json {
+                    {"finish_reason", nullptr},
+                    {"index", index},
+                    {"delta", delta},
+                },
+            })},
+            {"created", t},
+            {"id", oaicompat_cmpl_id},
+            {"model", oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object", "chat.completion.chunk"},
+        });
+    };
+    // We have to send an initial update to conform to openai behavior
+    if (first || is_progress) {
+        add_delta({
+            {"role", "assistant"},
+            {"content", nullptr},
+        });
+    }
+
+    for (const auto & diff : oaicompat_msg_diffs) {
+        add_delta(common_chat_msg_diff_to_json_oaicompat(diff));
+    }
+
+    if (!deltas.empty()) {
+        auto & last_json = deltas[deltas.size() - 1];
+        GGML_ASSERT(last_json.at("choices").size() >= 1);
+
+        if (prob_output.probs.size() > 0) {
+            last_json.at("choices").at(0)["logprobs"] = json {
+                {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
+            };
+        }
+
+        if (timings.prompt_n >= 0) {
+            last_json.push_back({"timings", timings.to_json()});
+        }
+        if (is_progress) {
+            last_json.push_back({"prompt_progress", progress.to_json()});
+        }
+    }
+
+    return deltas;
+}
+
+json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
+    std::vector<json> events;
+
+    if (n_decoded == 1) {
+        events.push_back(json {
+            {"event", "response.created"},
+            {"data", json {
+                {"type", "response.created"},
+                {"response", json {
+                    {"id",     oai_resp_id},
+                    {"object", "response"},
+                    {"status", "in_progress"},
+                }},
+            }},
+        });
+        events.push_back(json {
+            {"event", "response.in_progress"},
+            {"data", json {
+                {"type", "response.in_progress"},
+                {"response", json {
+                    {"id",     oai_resp_id},
+                    {"object", "response"},
+                    {"status", "in_progress"},
+                }},
+            }},
+        });
+    }
+
+    for (const common_chat_msg_diff & diff : oaicompat_msg_diffs) {
+        if (!diff.reasoning_content_delta.empty()) {
+            if (!thinking_block_started) {
+                events.push_back(json {
+                    {"event", "response.output_item.added"},
+                    {"data", json {
+                        {"type", "response.output_item.added"},
+                        {"item", json {
+                            {"id",                oai_resp_reasoning_id},
+                            {"summary",           json::array()},
+                            {"type",              "reasoning"},
+                            {"content",           json::array()},
+                            {"encrypted_content", ""},
+                            {"status",            "in_progress"},
+                        }},
+                    }},
+                });
+                thinking_block_started = true;
+            }
+            events.push_back(json {
+                {"event", "response.reasoning_text.delta"},
+                {"data", json {
+                    {"type",    "response.reasoning_text.delta"},
+                    {"delta",   diff.reasoning_content_delta},
+                    {"item_id", oai_resp_reasoning_id},
+                }},
+            });
+        }
+
+        if (!diff.content_delta.empty()) {
+            if (!text_block_started) {
+                events.push_back(json {
+                    {"event", "response.output_item.added"},
+                    {"data", json {
+                        {"type", "response.output_item.added"},
+                        {"item", json {
+                            {"content", json::array()},
+                            {"id",      oai_resp_message_id},
+                            {"role",    "assistant"},
+                            {"status",  "in_progress"},
+                            {"type",    "message"},
+                        }},
+                    }},
+                });
+                events.push_back(json {
+                    {"event", "response.content_part.added"},
+                    {"data", json {
+                        {"type",    "response.content_part.added"},
+                        {"item_id", oai_resp_message_id},
+                        {"part", json {
+                            {"type", "output_text"},
+                            {"text", ""},
+                        }},
+                    }},
+                });
+                text_block_started = true;
+            }
+            events.push_back(json {
+                {"event", "response.output_text.delta"},
+                {"data", json {
+                    {"type",    "response.output_text.delta"},
+                    {"item_id", oai_resp_message_id},
+                    {"delta",   diff.content_delta},
+                }},
+            });
+        }
+
+        if (!diff.tool_call_delta.name.empty()) {
+            events.push_back(json {
+                {"event", "response.output_item.added"},
+                {"data", json {
+                    {"type",  "response.output_item.added"},
+                    {"item", json {
+                        {"arguments", ""},
+                        {"call_id",   "fc_" + diff.tool_call_delta.id},
+                        {"name",      diff.tool_call_delta.name},
+                        {"type",      "function_call"},
+                        {"status",    "in_progress"},
+                    }},
+                }},
+            });
+            oai_resp_fc_id = diff.tool_call_delta.id;
+        }
+
+        if (!diff.tool_call_delta.arguments.empty()) {
+            events.push_back(json {
+                {"event", "response.function_call_arguments.delta"},
+                {"data", json {
+                    {"type",    "response.function_call_arguments.delta"},
+                    {"delta",   diff.tool_call_delta.arguments},
+                    {"item_id", "fc_" + oai_resp_fc_id},
+                }},
+            });
+        }
+    }
+    return events;
+}
+
+json server_task_result_cmpl_partial::to_json_anthropic() {
+    json events = json::array();
+    bool first = (n_decoded == 1);
+    // use member variables to track block state across streaming calls
+    // (anthropic_thinking_block_started, anthropic_text_block_started)
+
+    if (first) {
+        events.push_back({
+            {"event", "message_start"},
+            {"data", {
+                {"type", "message_start"},
+                {"message", {
+                    {"id", oaicompat_cmpl_id},
+                    {"type", "message"},
+                    {"role", "assistant"},
+                    {"content", json::array()},
+                    {"model", oaicompat_model},
+                    {"stop_reason", nullptr},
+                    {"stop_sequence", nullptr},
+                    {"usage", {
+                        {"input_tokens", n_prompt_tokens},
+                        {"output_tokens", 0}
+                    }}
+                }}
+            }}
+        });
+    }
+
+    // content block indices: thinking (0) -> text (0 or 1) -> tool_use (n+)
+    size_t thinking_block_index = 0;
+    // use anthropic_has_reasoning (set in update()) to know if ANY reasoning was generated
+    size_t text_block_index     = anthropic_has_reasoning ? 1 : 0;
+
+    // use local copies of streaming state (copied from task_result_state in update())
+    // these reflect the state BEFORE this chunk was processed
+    bool thinking_started = thinking_block_started;
+    bool text_started     = text_block_started;
+
+    for (const auto & diff : oaicompat_msg_diffs) {
+        // handle thinking/reasoning content
+        if (!diff.reasoning_content_delta.empty()) {
+            if (!thinking_started) {
+                events.push_back({
+                    {"event", "content_block_start"},
+                    {"data", {
+                        {"type", "content_block_start"},
+                        {"index", thinking_block_index},
+                        {"content_block", {
+                            {"type", "thinking"},
+                            {"thinking", ""}
+                        }}
+                    }}
+                });
+                thinking_started = true;
+            }
+
+            events.push_back({
+                {"event", "content_block_delta"},
+                {"data", {
+                    {"type", "content_block_delta"},
+                    {"index", thinking_block_index},
+                    {"delta", {
+                        {"type", "thinking_delta"},
+                        {"thinking", diff.reasoning_content_delta}
+                    }}
+                }}
+            });
+        }
+
+        // handle regular text content
+        if (!diff.content_delta.empty()) {
+            if (!text_started) {
+                events.push_back({
+                    {"event", "content_block_start"},
+                    {"data", {
+                        {"type", "content_block_start"},
+                        {"index", text_block_index},
+                        {"content_block", {
+                            {"type", "text"},
+                            {"text", ""}
+                        }}
+                    }}
+                });
+                text_started = true;
+            }
+
+            events.push_back({
+                {"event", "content_block_delta"},
+                {"data", {
+                    {"type", "content_block_delta"},
+                    {"index", text_block_index},
+                    {"delta", {
+                        {"type", "text_delta"},
+                        {"text", diff.content_delta}
+                    }}
+                }}
+            });
+        }
+
+        // handle tool calls
+        if (diff.tool_call_index != std::string::npos) {
+            // use anthropic_has_reasoning for thinking block count (persists across calls)
+            size_t content_block_index = (anthropic_has_reasoning ? 1 : 0) + (text_started ? 1 : 0) + diff.tool_call_index;
+
+            if (!diff.tool_call_delta.name.empty()) {
+                events.push_back({
+                    {"event", "content_block_start"},
+                    {"data", {
+                        {"type", "content_block_start"},
+                        {"index", content_block_index},
+                        {"content_block", {
+                            {"type", "tool_use"},
+                            {"id", diff.tool_call_delta.id},
+                            {"name", diff.tool_call_delta.name}
+                        }}
+                    }}
+                });
+            }
+
+            if (!diff.tool_call_delta.arguments.empty()) {
+                events.push_back({
+                    {"event", "content_block_delta"},
+                    {"data", {
+                        {"type", "content_block_delta"},
+                        {"index", content_block_index},
+                        {"delta", {
+                            {"type", "input_json_delta"},
+                            {"partial_json", diff.tool_call_delta.arguments}
+                        }}
+                    }}
+                });
+            }
+        }
+    }
+
+    return events;
+}
+
+//
+// server_task_result_embd
+//
+json server_task_result_embd::to_json() {
+    return res_type == TASK_RESPONSE_TYPE_OAI_EMBD
+        ? to_json_oaicompat()
+        : to_json_non_oaicompat();
+}
+
+json server_task_result_embd::to_json_non_oaicompat() {
+    return json {
+        {"index",     index},
+        {"embedding", embedding},
+    };
+}
+
+json server_task_result_embd::to_json_oaicompat() {
+    return json {
+        {"index",            index},
+        {"embedding",        embedding[0]},
+        {"tokens_evaluated", n_tokens},
+    };
+}
+
+//
+// server_task_result_rerank
+//
+json server_task_result_rerank::to_json() {
+    return json {
+        {"index",            index},
+        {"score",            score},
+        {"tokens_evaluated", n_tokens},
+    };
+}
+
+//
+// server_task_result_error
+//
+json server_task_result_error::to_json() {
+    json res = format_error_response(err_msg, err_type);
+    if (err_type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) {
+        res["n_prompt_tokens"] = n_prompt_tokens;
+        res["n_ctx"]           = n_ctx;
+    }
+    return res;
+}
+
+//
+// server_task_result_metrics
+//
+json server_task_result_metrics::to_json() {
+    return json {
+        { "idle",                            n_idle_slots },
+        { "processing",                      n_processing_slots },
+        { "deferred",                        n_tasks_deferred },
+        { "t_start",                         t_start },
+
+        { "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total },
+        { "t_tokens_generation_total",       t_tokens_generation_total },
+        { "n_tokens_predicted_total",        n_tokens_predicted_total },
+        { "t_prompt_processing_total",       t_prompt_processing_total },
+
+        { "n_tokens_max",                    n_tokens_max },
+
+        { "n_prompt_tokens_processed",       n_prompt_tokens_processed },
+        { "t_prompt_processing",             t_prompt_processing },
+        { "n_tokens_predicted",              n_tokens_predicted },
+        { "t_tokens_generation",             t_tokens_generation },
+
+        { "n_decode_total",                  n_decode_total },
+        { "n_busy_slots_total",              n_busy_slots_total },
+
+        { "slots",                           slots_data },
+    };
+}
+
+//
+// server_task_result_slot_save_load
+//
+json server_task_result_slot_save_load::to_json() {
+    if (is_save) {
+        return json {
+            { "id_slot",   id_slot },
+            { "filename",  filename },
+            { "n_saved",   n_tokens },
+            { "n_written", n_bytes },
+            { "timings", {
+                { "save_ms", t_ms }
+            }},
+        };
+    }
+
+    return json {
+        { "id_slot",    id_slot },
+        { "filename",   filename },
+        { "n_restored", n_tokens },
+        { "n_read",     n_bytes },
+        { "timings", {
+            { "restore_ms", t_ms }
+        }},
+    };
+}
+
+//
+// server_task_result_slot_erase
+//
+json server_task_result_slot_erase::to_json() {
+    return json {
+        { "id_slot",  id_slot },
+        { "n_erased", n_erased },
+    };
+}
+
+//
+// server_task_result_get_lora
+//
+
+json server_task_result_get_lora::to_json() {
+    json result = json::array();
+    for (size_t i = 0; i < loras.size(); ++i) {
+        auto & lora = loras[i];
+        json entry = {
+            {"id",            i},
+            {"path",          lora.info.path},
+            {"scale",         lora.info.scale},
+            {"task_name",     lora.info.task_name},
+            {"prompt_prefix", lora.info.prompt_prefix},
+        };
+        if (!lora.alora_invocation_tokens.empty()) {
+            entry["alora_invocation_string"] = lora.alora_invocation_string;
+            entry["alora_invocation_tokens"] = lora.alora_invocation_tokens;
+        }
+        result.push_back(std::move(entry));
+    }
+    return result;
+}
+
+//
+// server_task_result_apply_lora
+//
+
+json server_task_result_apply_lora::to_json() {
+    return json {{ "success", true }};
+}
+
+//
+// server_prompt_cache
+//
+size_t server_prompt_cache::size() const {
+    size_t res = 0;
+
+    for (const auto & state : states) {
+        res += state.size();
+    }
+
+    return res;
+}
+
+size_t server_prompt_cache::n_tokens() const {
+    size_t res = 0;
+
+    for (const auto & state : states) {
+        res += state.n_tokens();
+    }
+
+    return res;
+}
+
+server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t state_size) {
+    // first check if the current state is contained fully in the cache
+    for (auto it = states.begin(); it != states.end(); ++it) {
+        const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens);
+
+        if (cur_lcp_len == (int) prompt.tokens.size()) {
+            SRV_WRN("%s", " - prompt is already in the cache, skipping\n");
+            return nullptr;
+        }
+    }
+
+    // next, remove any cached prompts that are fully contained in the current prompt
+    for (auto it = states.begin(); it != states.end();) {
+        const int len = it->tokens.get_common_prefix(prompt.tokens);
+
+        if (len == (int) it->tokens.size()) {
+            SRV_WRN(" - removing obsolete cached prompt with length %d\n", len);
+
+            it = states.erase(it);
+        } else {
+            ++it;
+        }
+    }
+
+    std::vector<uint8_t> state_data;
+
+    // check if we can allocate enough memory for the new state
+    try {
+        state_data.resize(state_size);
+    } catch (const std::bad_alloc & e) {
+        SRV_ERR("failed to allocate memory for prompt cache state: %s\n", e.what());
+
+        limit_size = std::max<size_t>(1, 0.4*size());
+
+        SRV_WRN(" - cache size limit reduced to %.3f MiB\n", limit_size / (1024.0 * 1024.0));
+
+        update();
+
+        return nullptr;
+    }
+
+    // TODO: for some reason we can't copy server_tokens, so we have to do this workaround
+    auto & cur = states.emplace_back();
+    cur = {
+        /*.tokens      =*/ server_tokens(prompt.tokens.get_text_tokens(), false),
+        /*.data        =*/ std::move(state_data),
+        /*.checkpoints =*/ prompt.checkpoints,
+    };
+
+    return &cur;
+}
+
+bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot) {
+    const int lcp_best = prompt.tokens.get_common_prefix(tokens_new);
+
+    float f_keep_best = float(lcp_best) / prompt.tokens.size();
+    float sim_best    = float(lcp_best) / tokens_new.size();
+
+    SRV_WRN(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+
+    auto it_best = states.end();
+
+    // find the most similar cached prompt, that would also preserve the most context
+    for (auto it = states.begin(); it != states.end(); ++it) {
+        const int lcp_cur = it->tokens.get_common_prefix(tokens_new);
+
+        const float f_keep_cur = float(lcp_cur) / it->tokens.size();
+        const float sim_cur    = float(lcp_cur) / tokens_new.size();
+
+        // don't trash large prompts
+        if (f_keep_cur < 0.25f) {
+            continue;
+        }
+
+        if (f_keep_best < f_keep_cur && sim_best < sim_cur) {
+            f_keep_best = f_keep_cur;
+            sim_best    = sim_cur;
+
+            it_best = it;
+        }
+    }
+
+    if (it_best != states.end()) {
+        SRV_WRN(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+
+        const size_t size = it_best->data.size();
+        const size_t n = llama_state_seq_set_data_ext(ctx, it_best->data.data(), size, id_slot, 0);
+        if (n != size) {
+            SRV_WRN("failed to restore state with size %zu\n", size);
+
+            return false;
+        }
+
+        it_best->data.clear();
+        it_best->data.shrink_to_fit();
+
+        prompt = std::move(*it_best);
+
+        states.erase(it_best);
+    }
+
+    return true;
+}
+
+void server_prompt_cache::update() {
+    if (limit_size > 0) {
+        // always keep at least one state, regardless of the limits
+        while (states.size() > 1 && size() > limit_size) {
+            if (states.empty()) {
+                break;
+            }
+
+            SRV_WRN(" - cache size limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0));
+
+            states.pop_front();
+        }
+    }
+
+    // average size per token
+    const float size_per_token = std::max<float>(1.0f, float(size()) / (std::max<size_t>(1, n_tokens())));
+
+    // dynamically increase the token limit if it can fit in the memory limit
+    const size_t limit_tokens_cur = limit_size > 0 ? std::max<size_t>(limit_tokens, limit_size/size_per_token) : limit_tokens;
+
+    if (limit_tokens > 0) {
+        while (states.size() > 1 && n_tokens() > limit_tokens_cur) {
+            if (states.empty()) {
+                break;
+            }
+
+            SRV_WRN(" - cache token limit (%zu, est: %zu) reached, removing oldest entry (size = %.3f MiB)\n",
+                    limit_tokens, limit_tokens_cur, states.front().size() / (1024.0 * 1024.0));
+
+            states.pop_front();
+        }
+    }
+
+    SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
+            states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur);
+
+    for (const auto & state : states) {
+        SRV_WRN("   - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
+                (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0));
+    }
+}