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Server: Don't ignore llama.cpp params (#8754)
* Don't ignore llama.cpp params * Add fallback for max_tokens
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@ -900,7 +900,7 @@ struct server_context {
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slot.params.stream = json_value(data, "stream", false);
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slot.params.cache_prompt = json_value(data, "cache_prompt", false);
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slot.params.n_predict = json_value(data, "n_predict", default_params.n_predict);
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slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict));
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slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
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slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
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slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
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@ -355,24 +355,6 @@ static json oaicompat_completion_params_parse(
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llama_params["__oaicompat"] = true;
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// Map OpenAI parameters to llama.cpp parameters
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//
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// For parameters that are defined by the OpenAI documentation (e.g.
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// temperature), we explicitly specify OpenAI's intended default; we
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// need to do that because sometimes OpenAI disagrees with llama.cpp
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//
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// https://platform.openai.com/docs/api-reference/chat/create
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llama_sampling_params default_sparams;
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llama_params["model"] = json_value(body, "model", std::string("unknown"));
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llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
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llama_params["logit_bias"] = json_value(body, "logit_bias", json::object());
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llama_params["n_predict"] = json_value(body, "max_tokens", -1);
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llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
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llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED);
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llama_params["stream"] = json_value(body, "stream", false);
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llama_params["temperature"] = json_value(body, "temperature", 1.0);
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llama_params["top_p"] = json_value(body, "top_p", 1.0);
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// Apply chat template to the list of messages
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llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));
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