--- /dev/null
+#include "server-schema.h"
+
+#include "json-schema-to-grammar.h"
+
+namespace server_schema {
+
+//
+// llama.cpp-specific completion schema
+//
+
+std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(const common_params & params_base, task_params & params) {
+ std::vector<std::unique_ptr<field>> fields;
+ auto add = [&](field * f) {
+ fields.emplace_back(f);
+ };
+
+ add((new field_bool("timings_per_token", params.timings_per_token))
+ ->set_desc("Include prompt processing and text generation speed information in each response"));
+
+ add((new field_bool("stream", params.stream))
+ ->set_desc("Allows receiving each predicted token in real-time instead of waiting for the completion to finish"));
+
+ add((new field_nested("stream_options"))
+ ->add_subfield((new field_bool("include_usage", params.include_usage))
+ ->set_desc("Whether to include usage information in the stream"))
+ ->set_desc("Additional options for streaming responses"));
+
+ add((new field_bool("cache_prompt", params.cache_prompt))
+ ->set_desc("Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests"));
+
+ add((new field_bool("return_tokens", params.return_tokens))
+ ->set_desc("Return the raw generated token ids in the `tokens` field"));
+
+ add((new field_bool("return_progress", params.return_progress))
+ ->set_desc("Include prompt processing progress events in stream mode"));
+
+ add((new field_num("n_predict", params.n_predict))
+ ->set_hard_limits(-1, INT32_MAX)
+ ->add_alias("max_completion_tokens")
+ ->add_alias("max_tokens")
+ ->set_desc("Set the maximum number of tokens to predict. When 0, no tokens will be generated but the prompt is evaluated into the cache"));
+
+ add((new field_num("n_indent", params.n_indent))
+ ->set_hard_limits(0, INT32_MAX)
+ ->set_desc("Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks"));
+
+ add((new field_num("n_keep", params.n_keep))
+ ->set_hard_limits(-1, INT32_MAX)
+ ->set_desc("Specify the number of tokens from the initial prompt to retain when context size is exceeded. Use -1 to retain all tokens from the prompt"));
+
+ add((new field_num("n_discard", params.n_discard))
+ ->set_hard_limits(0, INT32_MAX)
+ ->set_desc("Number of tokens after n_keep that may be discarded when shifting context (0 = half context)"));
+
+ add((new field_num("n_cmpl", params.n_cmpl))
+ ->set_hard_limits(1, params_base.n_parallel)
+ ->add_alias("n") // alias "n" as fallback (OpenAI completions API)
+ ->set_desc("Number of completions to generate. If the input has multiple prompts, total outputs will be N prompts times n_cmpl"));
+
+ add((new field_num("n_cache_reuse", params.n_cache_reuse))
+ ->set_hard_limits(0, INT32_MAX)
+ ->set_desc("Min chunk size to attempt reusing from the cache via KV shifting. See --cache-reuse arg"));
+
+ // TODO: implement t_max_prompt_ms
+ // add((new field_num("t_max_prompt_ms", params.t_max_prompt_ms))
+
+ add((new field_num("t_max_predict_ms", params.t_max_predict_ms))
+ ->set_hard_limits(-1, std::numeric_limits<int64_t>::max())
+ ->set_desc("Set a time limit in milliseconds for the prediction phase. The timeout triggers if generation exceeds this time (measured since the first token) and a newline has been generated. Useful for FIM applications"));
+
+ add((new field_json("response_fields"))
+ ->set_desc("A list of response fields to return. Missing fields are omitted without error. Fields with a slash are unnested (e.g. generation_settings/n_predict moves n_predict to the root)")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ ctx.params.response_fields = json_value(data, "response_fields", std::vector<std::string>());
+ }));
+
+
+ //
+ // Sampling params
+ //
+
+ add((new field_num("top_k", params.sampling.top_k))
+ ->set_limits(0, INT32_MAX)
+ ->set_desc("Limit the next token selection to the K most probable tokens (0 = disabled)"));
+
+ add((new field_num("top_p", params.sampling.top_p))
+ ->set_limits(0.0f, 1.0f)
+ ->set_desc("Limit the next token selection to a subset of tokens with cumulative probability above threshold P (1.0 = disabled)"));
+
+ add((new field_num("min_p", params.sampling.min_p))
+ ->set_limits(0.0f, 1.0f)
+ ->set_desc("The minimum probability for a token to be considered, relative to the probability of the most likely token (0 = disabled)"));
+
+ add((new field_num("top_n_sigma", params.sampling.top_n_sigma))
+ ->set_desc("Keep tokens within n standard deviations of the top token logit (< 0 = disabled)"));
+
+ add((new field_num("xtc_probability", params.sampling.xtc_probability))
+ ->set_limits(0.0f, 1.0f)
+ ->set_desc("Set the chance for token removal via XTC sampler (0 = disabled)"));
+
+ add((new field_num("xtc_threshold", params.sampling.xtc_threshold))
+ ->set_limits(0.0f, 1.0f)
+ ->set_desc("Set a minimum probability threshold for tokens to be removed via XTC sampler (> 0.5 disables XTC)"));
+
+ add((new field_num("typical_p", params.sampling.typ_p))
+ // ->set_limits(0.0f, 1.0f) // what's the valid range?
+ ->set_desc("Enable locally typical sampling with parameter p (1.0 = disabled)"));
+
+ add((new field_num("temperature", params.sampling.temp))
+ ->set_limits(0.0f, std::numeric_limits<float>::infinity())
+ ->set_desc("Adjust the randomness of the generated text (0 = greedy)"));
+
+ add((new field_num("dynatemp_range", params.sampling.dynatemp_range))
+ ->set_desc("Dynamic temperature range. The final temperature will be in [temperature - range, temperature + range] (0 = disabled)"));
+
+ add((new field_num("dynatemp_exponent", params.sampling.dynatemp_exponent))
+ ->set_desc("Dynamic temperature exponent, controls how entropy maps to temperature"));
+
+ add((new field_num("repeat_last_n", params.sampling.penalty_last_n))
+ ->set_hard_limits(-1, INT32_MAX)
+ ->set_desc("Last n tokens to consider for penalizing repetition (0 = disabled, -1 = ctx-size)"));
+
+ add((new field_num("repeat_penalty", params.sampling.penalty_repeat))
+ ->set_desc("Control the repetition of token sequences in the generated text (1.0 = disabled)"));
+
+ add((new field_num("frequency_penalty", params.sampling.penalty_freq))
+ ->set_desc("Repeat alpha frequency penalty (0 = disabled)"));
+
+ add((new field_num("presence_penalty", params.sampling.penalty_present))
+ ->set_desc("Repeat alpha presence penalty (0 = disabled)"));
+
+ add((new field_num("dry_multiplier", params.sampling.dry_multiplier))
+ ->set_desc("Set the DRY (Don't Repeat Yourself) repetition penalty multiplier (0 = disabled)"));
+
+ add((new field_num("dry_base", params.sampling.dry_base))
+ ->set_desc("Set the DRY repetition penalty base value (must be >= 1.0, any values < 1.0 will be replaced with the default value)")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ float v = data.at("dry_base").get<float>();
+ ctx.params.sampling.dry_base = (v < 1.0f) ? params_base.sampling.dry_base : v;
+ }));
+
+ add((new field_num("dry_allowed_length", params.sampling.dry_allowed_length))
+ ->set_hard_limits(0, INT32_MAX)
+ ->set_desc("Tokens that extend repetition beyond this length receive exponentially increasing penalty: multiplier * base ^ (sequence_length - allowed_length)"));
+
+ add((new field_num("dry_penalty_last_n", params.sampling.dry_penalty_last_n))
+ ->set_hard_limits(-1, INT32_MAX)
+ ->set_desc("How many tokens to scan for repetitions (0 = disabled, -1 = context size)"));
+
+ add((new field_num("mirostat", params.sampling.mirostat))
+ ->set_limits(0, 2)
+ ->set_desc("Enable Mirostat sampling, controlling perplexity during text generation (0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"));
+
+ add((new field_num("mirostat_tau", params.sampling.mirostat_tau))
+ ->set_desc("Set the Mirostat target entropy, parameter tau"));
+
+ add((new field_num("mirostat_eta", params.sampling.mirostat_eta))
+ ->set_desc("Set the Mirostat learning rate, parameter eta"));
+
+ add((new field_num("adaptive_target", params.sampling.adaptive_target))
+ ->set_limits(-std::numeric_limits<float>::max(), 1.0f)
+ ->set_desc("Adaptive sampling target entropy (valid range 0.0 to 1.0; negative = disabled)"));
+
+ add((new field_num("adaptive_decay", params.sampling.adaptive_decay))
+ ->set_hard_limits(0.0f, 0.99f)
+ ->set_desc("EMA decay for adaptive sampling; history approximates 1/(1-decay) tokens"));
+
+ // seed is uint32_t; field_num uses int32_t so use a handler
+ add((new field_num("seed", params.sampling.seed))
+ ->set_desc("Set the random number generator (RNG) seed (-1 = random)"));
+
+ add((new field_num("n_probs", params.sampling.n_probs))
+ ->add_alias("logprobs") // use "logprobs" if "n_probs" wasn't provided
+ ->set_desc("If greater than 0, output the probabilities of top N tokens for each generated token"));
+
+ add((new field_num("min_keep", params.sampling.min_keep))
+ ->set_hard_limits(0, INT32_MAX)
+ ->set_desc("If greater than 0, force samplers to return at least N possible tokens"));
+
+ add((new field_bool("backend_sampling", params.sampling.backend_sampling))
+ ->set_desc("Use backend sampling instead of llama.cpp sampling"));
+
+ add((new field_bool("post_sampling_probs", params.post_sampling_probs))
+ ->set_desc("Return probabilities of top n_probs tokens after applying the sampling chain"));
+
+ //
+ // Speculative decoding params
+ //
+
+ // TODO: to keep things simple, we disable speculative parameter adjustments for now
+#if 0
+ // TODO: for now, be able to adjust only the draft-model based speculative parameters
+ add((new field_num("speculative.n_max", params.speculative.draft.n_max))
+ ->set_hard_limits(0, INT32_MAX)
+ ->set_desc("Maximum number of tokens to draft during speculative decoding"));
+
+ add((new field_num("speculative.n_min", params.speculative.draft.n_min))
+ ->set_hard_limits(0, INT32_MAX)
+ ->set_desc("Minimum number of draft tokens to use for speculative decoding");
+
+ add((new field_num("speculative.p_min", params.speculative.draft.p_min))
+ ->set_hard_limits(0.0f, 1.0f)
+ ->set_desc("Minimum speculative decoding probability for draft tokens (0 = greedy)"));
+
+ add((new field_str("speculative.type"))
+ ->set_desc("Speculative decoding method (for debugging and research purposes)")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ ctx.params.speculative.types = { common_speculative_type_from_name(data.at("speculative.type").get<std::string>()) };
+ }));
+
+ add((new field_num("speculative.ngram_size_n", params.speculative.ngram_simple.size_n))
+ ->set_desc("Ngram size for lookup in ngram-based speculative decoding"));
+
+ add((new field_num("speculative.ngram_size_m", params.speculative.ngram_simple.size_m))
+ ->set_desc("Mgram size for speculative tokens in ngram-based speculative decoding"));
+
+ add((new field_num("speculative.ngram_min_hits", params.speculative.ngram_simple.min_hits))
+ ->set_desc("Minimum hits at ngram lookup for mgram to be proposed"));
+#endif
+
+ add((new field_json("lora"))
+ ->set_desc("A list of LoRA adapters to apply to this request. Each entry must have `id` and `scale` fields. Adapters not listed default to scale 0.0")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ const auto & lora = data.at("lora");
+ if (!lora.is_array()) {
+ throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
+ }
+ ctx.params.lora = parse_lora_request(lora);
+ }));
+
+ // 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
+ add((new field_json("dry_sequence_breakers"))
+ ->set_desc("Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ ctx.params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
+ if (ctx.params.sampling.dry_sequence_breakers.empty()) {
+ throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
+ }
+ }));
+
+ // handle both "json_schema" and "grammar"
+ add((new field_json("json_schema"))
+ ->add_alias("grammar")
+ ->set_desc("Set a JSON schema (json_schema) or GBNF grammar string (grammar) for constrained generation. json_schema takes precedence if both are provided")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ auto & params = ctx.params;
+ 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());
+ std::string grammar_str = json_schema_to_grammar(schema);
+ SRV_DBG("Converted grammar: %s\n", grammar_str.c_str());
+ params.sampling.grammar = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, std::move(grammar_str)};
+ } catch (const std::exception & e) {
+ throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
+ }
+ } else {
+ std::string grammar_str = json_value(data, "grammar", std::string());
+ if (!grammar_str.empty()) {
+ // grammar_type key is set by the server when converting chat template grammars
+ std::string grammar_type = json_value(data, "grammar_type", std::string());
+ if (grammar_type == "tool_calls") {
+ params.sampling.grammar = {COMMON_GRAMMAR_TYPE_TOOL_CALLS, std::move(grammar_str)};
+ } else {
+ // explicit grammar from the user (API field "grammar")
+ params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, std::move(grammar_str)};
+ }
+ SRV_DBG("Grammar (%s): %s\n", grammar_type.c_str(), common_grammar_value(params.sampling.grammar).c_str());
+ }
+ }
+ }));
+
+ add((new field_bool("grammar_lazy", params.sampling.grammar_lazy))
+ ->set_desc("Whether to apply grammar constraints lazily, only when triggered (instead of at every step)"));
+
+ //
+ // Chat parser params
+ //
+
+ // TODO: change this to string field instead
+ add((new field_json("chat_format"))
+ ->set_desc("Chat format used internally by the server")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ ctx.params.chat_parser_params.format = static_cast<common_chat_format>(data.at("chat_format").get<int>());
+ SRV_INF("Chat format: %s\n", common_chat_format_name(ctx.params.chat_parser_params.format));
+ }));
+
+ add((new field_str("reasoning_format"))
+ ->set_desc("Reasoning format for chain-of-thought models")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ auto reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
+ ctx.params.chat_parser_params.reasoning_format = reasoning_format;
+ ctx.params.chat_parser_params.reasoning_in_content = ctx.params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
+ }));
+
+ add((new field_str("generation_prompt"))
+ ->set_desc("Generation prompt appended to the chat template output")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ std::string s = data.at("generation_prompt").get<std::string>();
+ ctx.params.chat_parser_params.generation_prompt = s;
+ ctx.params.sampling.generation_prompt = s;
+ }));
+
+ add((new field_bool("parse_tool_calls", params.chat_parser_params.parse_tool_calls))
+ ->set_desc("Whether to parse tool calls from the generated output"));
+
+ add((new field_str("chat_parser"))
+ ->set_desc("Chat parser configuration string")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ ctx.params.chat_parser_params.parser.load(data.at("chat_parser").get<std::string>());
+ }));
+
+ add((new field_json("continue_final_message"))
+ ->set_desc("Whether to continue the final message of the chat template")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ auto continuation = common_chat_continuation_parse(data.at("continue_final_message"));
+ ctx.params.chat_parser_params.is_continuation = continuation != COMMON_CHAT_CONTINUATION_NONE;
+ }));
+
+ add((new field_bool("echo", params.chat_parser_params.echo))
+ ->set_desc("Whether to echo the input tokens in the output"));
+
+ //
+ // Token-level fields (require vocab)
+ //
+
+ add((new field_json("preserved_tokens"))
+ ->set_desc("List of token strings that must not be split during tokenization")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(ctx.vocab != nullptr);
+ for (const auto & t : data.at("preserved_tokens")) {
+ auto ids = common_tokenize(ctx.vocab, t.get<std::string>(), false, true);
+ if (ids.size() == 1) {
+ ctx.params.sampling.preserved_tokens.insert(ids[0]);
+ }
+ }
+ }));
+
+ add((new field_json("grammar_triggers"))
+ ->set_desc("List of strings or patterns that trigger grammar-constrained generation")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(ctx.vocab != nullptr);
+ for (const auto & t : data.at("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(ctx.vocab, word, false, true);
+ if (ids.size() == 1) {
+ auto token = ids[0];
+ if (std::find(ctx.params.sampling.preserved_tokens.begin(), ctx.params.sampling.preserved_tokens.end(), (llama_token) token) == ctx.params.sampling.preserved_tokens.end()) {
+ throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
+ }
+ common_grammar_trigger trigger;
+ trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
+ trigger.value = word;
+ trigger.token = token;
+ ctx.params.sampling.grammar_triggers.push_back(std::move(trigger));
+ } else {
+ ctx.params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
+ }
+ } else {
+ ctx.params.sampling.grammar_triggers.emplace_back(std::move(ct.value));
+ }
+ }
+ if (ctx.params.sampling.grammar_lazy && ctx.params.sampling.grammar_triggers.empty()) {
+ throw std::runtime_error("Error: no triggers set for lazy grammar!");
+ }
+ }));
+
+ add((new field_bool("reasoning_control", params.sampling.reasoning_control))
+ ->set_desc("Create the budget sampler on demand so reasoning can be ended at runtime"));
+
+ add((new field_num("reasoning_budget_tokens", params.sampling.reasoning_budget_tokens))
+ ->set_hard_limits(-1, INT32_MAX)
+ ->set_desc("Number of tokens in the reasoning budget (-1 = disabled)"));
+
+ add((new field_str("reasoning_budget_start_tag"))
+ ->set_desc("Token string marking the start of the reasoning budget section")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(ctx.vocab != nullptr);
+ ctx.params.sampling.reasoning_budget_start = common_tokenize(ctx.vocab, data.at("reasoning_budget_start_tag").get<std::string>(), false, true);
+ }));
+
+ add((new field_str("reasoning_budget_end_tag"))
+ ->set_desc("Token string marking the end of the reasoning budget section")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(ctx.vocab != nullptr);
+ std::string end_tag = data.at("reasoning_budget_end_tag").get<std::string>();
+ ctx.params.sampling.reasoning_budget_end = common_tokenize(ctx.vocab, end_tag, false, true);
+ }));
+
+ add((new field_str("reasoning_budget_message"))
+ ->set_desc("Message to prepend to the reasoning budget end tag when forcing it")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(ctx.vocab != nullptr);
+ std::string end_tag = json_value(data, "reasoning_budget_end_tag", std::string());
+ std::string message = data.at("reasoning_budget_message").get<std::string>();
+ ctx.params.sampling.reasoning_budget_forced = common_tokenize(ctx.vocab, message + end_tag, false, true);
+ }));
+
+ add((new field_json("logit_bias"))
+ ->set_desc("Modify the likelihood of specific tokens. Accepts an array of [token, bias] pairs or an object mapping token to bias. Use false as bias to ban a token")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(ctx.vocab != nullptr);
+ ctx.params.sampling.logit_bias.clear();
+ const auto & logit_bias = data.at("logit_bias");
+ const int n_vocab = llama_vocab_n_tokens(ctx.vocab);
+ auto parse_bias = [](const json & v, float & bias) -> bool {
+ if (v.is_number()) { bias = v.get<float>(); return true; }
+ if (v.is_boolean() && !v.get<bool>()) { bias = -INFINITY; return true; }
+ return false;
+ };
+ if (logit_bias.is_array()) {
+ for (const auto & el : logit_bias) {
+ if (!el.is_array() || el.size() != 2) continue;
+ float bias;
+ if (!parse_bias(el[1], bias)) continue;
+ if (el[0].is_number_integer()) {
+ llama_token tok = el[0].get<llama_token>();
+ if (tok >= 0 && tok < n_vocab) ctx.params.sampling.logit_bias.push_back({tok, bias});
+ } else if (el[0].is_string()) {
+ for (auto tok : common_tokenize(ctx.vocab, el[0].get<std::string>(), false))
+ ctx.params.sampling.logit_bias.push_back({tok, bias});
+ }
+ }
+ } else if (logit_bias.is_object()) {
+ for (const auto & el : logit_bias.items()) {
+ float bias;
+ if (!parse_bias(el.value(), bias)) continue;
+ char * end;
+ llama_token tok = strtol(el.key().c_str(), &end, 10);
+ if (*end == 0) {
+ if (tok >= 0 && tok < n_vocab) ctx.params.sampling.logit_bias.push_back({tok, bias});
+ } else {
+ for (auto t : common_tokenize(ctx.vocab, el.key(), false))
+ ctx.params.sampling.logit_bias.push_back({t, bias});
+ }
+ }
+ }
+ }));
+
+ add((new field_bool("ignore_eos", params.sampling.ignore_eos))
+ ->set_desc("Ignore the end-of-sequence token and continue generating")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(ctx.logit_bias_eog != nullptr);
+ ctx.params.sampling.ignore_eos = data.at("ignore_eos").get<bool>();
+ if (ctx.params.sampling.ignore_eos && ctx.logit_bias_eog) {
+ ctx.params.sampling.logit_bias.insert(
+ ctx.params.sampling.logit_bias.end(),
+ ctx.logit_bias_eog->begin(), ctx.logit_bias_eog->end());
+ }
+ }));
+
+ add((new field_json("stop"))
+ ->set_desc("Specify stopping strings. Generation stops when one is produced, and the string is not included in the output")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ ctx.params.antiprompt.clear();
+ const auto & stop = data.at("stop");
+ if (stop.is_array()) {
+ for (const auto & word : stop) {
+ if (!word.empty()) ctx.params.antiprompt.push_back(word);
+ }
+ } else if (stop.is_string()) {
+ ctx.params.antiprompt.push_back(stop.get<std::string>());
+ }
+ // fall back to CLI defaults if the request provided no effective stop strings
+ if (ctx.params.antiprompt.empty()) {
+ ctx.params.antiprompt = params_base.antiprompt;
+ }
+ }));
+
+ add((new field_json("samplers"))
+ ->set_desc("The order in which samplers are applied. An array of sampler type names, or a single string of sampler chars")
+ ->set_handler([&](field_eval_context & ctx, const json & data) {
+ const auto & samplers = data.at("samplers");
+ if (samplers.is_array()) {
+ ctx.params.sampling.samplers = common_sampler_types_from_names(samplers);
+ } else if (samplers.is_string()) {
+ ctx.params.sampling.samplers = common_sampler_types_from_chars(samplers.get<std::string>());
+ }
+ }));
+
+ return fields;
+}
+
+task_params eval_llama_cmpl_schema(
+ const llama_vocab * vocab,
+ const common_params & params_base,
+ const int n_ctx_slot,
+ const std::vector<llama_logit_bias> & logit_bias_eog,
+ const json & data) {
+ task_params params;
+
+ // Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
+ params.sampling = params_base.sampling;
+ params.speculative = params_base.speculative;
+ params.n_keep = params_base.n_keep;
+ params.n_predict = params_base.n_predict;
+ params.n_cache_reuse = params_base.n_cache_reuse;
+ params.cache_prompt = params_base.cache_prompt;
+ params.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.chat_parser_params.reasoning_format = params_base.reasoning_format;
+
+ // create context and schema
+ field_eval_context ctx(params);
+ ctx.vocab = vocab;
+ ctx.logit_bias_eog = &logit_bias_eog;
+
+ auto schema = make_llama_cmpl_schema(params_base, params);
+
+ // eval all fields in the schema
+ for (const auto & f : schema) {
+ f->eval(ctx, data);
+ }
+
+ // post-processing
+ {
+ 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 "reasoning_format" is not provided, its handler will not be called, we will need to handle it here
+ auto reasoning_format = params.chat_parser_params.reasoning_format;
+ params.chat_parser_params.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
+ }
+
+ // debugging
+ {
+ auto budget = params.sampling.reasoning_budget_tokens;
+ SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu toks, forced=%zu toks\n",
+ budget, params.sampling.generation_prompt.c_str(),
+ params.sampling.reasoning_budget_start.size(),
+ params.sampling.reasoning_budget_end.size(),
+ params.sampling.reasoning_budget_forced.size());
+ }
+
+ return params;
+}
+
+//
+// eval() implementations
+//
+
+static void handle_with_catch(const char * name, std::function<void()> func) {
+ try {
+ func();
+ } catch (const std::exception & e) {
+ throw std::invalid_argument(string_format("Field '%s': %s", name, e.what()));
+ }
+}
+
+template <typename T>
+void field_num<T>::eval(field_eval_context & ctx, const json & data) {
+ for (const auto & n : name) {
+ if (data.contains(n)) {
+ handle_with_catch(n, [&]() {
+ if (custom_handler) {
+ custom_handler(ctx, data);
+ } else if (!is_hard_limit) {
+ val = std::max(min, std::min(max, data.at(n).template get<T>()));
+ } else {
+ T tmp = data.at(n).template get<T>();
+ if (tmp < min || tmp > max) {
+ throw std::invalid_argument(std::string("Value must be between ") + std::to_string(min) + " <= value <= " + std::to_string(max) + ", but got " + std::to_string(tmp));
+ }
+ val = tmp;
+ }
+ });
+ return;
+ }
+ }
+}
+
+void field_str::eval(field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(custom_handler);
+ for (const auto & n : name) {
+ if (data.contains(n)) {
+ handle_with_catch(n, [&]() {
+ custom_handler(ctx, data);
+ });
+ return;
+ }
+ }
+}
+
+void field_bool::eval(field_eval_context & ctx, const json & data) {
+ for (const auto & n : name) {
+ if (data.contains(n)) {
+ handle_with_catch(n, [&]() {
+ if (custom_handler) {
+ custom_handler(ctx, data);
+ } else {
+ val = data.at(n).get<bool>();
+ }
+ });
+ return;
+ }
+ }
+}
+
+void field_json::eval(field_eval_context & ctx, const json & data) {
+ GGML_ASSERT(custom_handler);
+ for (const auto & n : name) {
+ if (data.contains(n)) {
+ handle_with_catch(n, [&]() {
+ custom_handler(ctx, data);
+ });
+ return;
+ }
+ }
+}
+
+void field_nested::eval(field_eval_context & ctx, const json & data) {
+ for (const auto & n : name) {
+ if (data.contains(n) && data.at(n).is_object()) {
+ for (auto & f : subfields) {
+ f->eval(ctx, data.at(n));
+ }
+ return;
+ }
+ }
+}
+
+} // namespace server_schema
}
//
-// 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 std::vector<llama_logit_bias> & logit_bias_eog,
- 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);
- auto max_tokens = json_value(data, "max_tokens", defaults.n_predict);
- params.n_predict = json_value(data, "n_predict", json_value(data, "max_completion_tokens", max_tokens));
- 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_discard = std::max(0, params.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 = defaults.speculative;
-
- // TODO: to keep things simple, we disable speculative parameter adjustments for now
-#if 0
- // TODO: for now, be able to adjust only the draft-model based speculative parameters
- params.speculative.draft.n_min = json_value(data, "speculative.n_min", defaults.speculative.draft.n_min);
- params.speculative.draft.n_max = json_value(data, "speculative.n_max", defaults.speculative.draft.n_max);
- params.speculative.draft.p_min = json_value(data, "speculative.p_min", defaults.speculative.draft.p_min);
-
- params.speculative.draft.n_min = std::min(params.speculative.draft.n_max, params.speculative.draft.n_min);
- params.speculative.draft.n_min = std::max(params.speculative.draft.n_min, 0);
- params.speculative.draft.n_max = std::max(params.speculative.draft.n_max, 0);
-
- // for debugging and research purposes
- 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);
-#endif
-
- // 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());
- std::string grammar_str = json_schema_to_grammar(schema);
- SRV_DBG("Converted grammar: %s\n", grammar_str.c_str());
- params.sampling.grammar = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, std::move(grammar_str)};
- } catch (const std::exception & e) {
- throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
- }
- } else {
- params.sampling.grammar = defaults.sampling.grammar;
-
- std::string grammar_str = json_value(data, "grammar", std::string());
- if (!grammar_str.empty()) {
- // grammar_type key is set by the server when converting chat template grammars
- std::string grammar_type = json_value(data, "grammar_type", std::string());
- if (grammar_type == "tool_calls") {
- params.sampling.grammar = {COMMON_GRAMMAR_TYPE_TOOL_CALLS, std::move(grammar_str)};
- } else {
- // explicit grammar from the user (API field "grammar")
- params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, std::move(grammar_str)};
- }
- SRV_DBG("Grammar (%s): %s\n", grammar_type.c_str(), common_grammar_value(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.generation_prompt = json_value(data, "generation_prompt", std::string());
- params.sampling.generation_prompt = params.chat_parser_params.generation_prompt;
- SRV_DBG("Generation prompt: '%s'\n", params.chat_parser_params.generation_prompt.c_str());
- 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>());
- }
- if (data.contains("continue_final_message")) {
- auto continuation = common_chat_continuation_parse(data.at("continue_final_message"));
- params.chat_parser_params.is_continuation = continuation != COMMON_CHAT_CONTINUATION_NONE;
- }
- params.chat_parser_params.echo = json_value(data, "echo", false);
- }
-
- {
- 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!");
- }
- }
-
- // Parse reasoning budget sampler parameters
- {
- const int32_t budget = json_value(data, "reasoning_budget_tokens", (int32_t) -1);
- const auto start_tag = json_value(data, "reasoning_budget_start_tag", std::string());
- const auto end_tag = json_value(data, "reasoning_budget_end_tag", std::string());
- const auto message = json_value(data, "reasoning_budget_message", std::string());
- params.sampling.reasoning_budget_tokens = budget;
- params.sampling.reasoning_control = json_value(data, "reasoning_control", false);
-
- if (!start_tag.empty()) {
- params.sampling.reasoning_budget_start = common_tokenize(vocab, start_tag, false, true);
- }
- if (!end_tag.empty()) {
- params.sampling.reasoning_budget_end = common_tokenize(vocab, end_tag, false, true);
- params.sampling.reasoning_budget_forced = common_tokenize(vocab, message + end_tag, false, true);
-
- SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu toks, forced=%zu toks\n",
- budget, params.sampling.generation_prompt.c_str(),
- params.sampling.reasoning_budget_start.size(),
- params.sampling.reasoning_budget_end.size(),
- params.sampling.reasoning_budget_forced.size());
- }
- }
-
- {
- 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(),
- logit_bias_eog.begin(), 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);
- } 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
//
overview / pkg / ggml / sources / llama.cpp / commitdiff