mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-08 07:10:32 +00:00
105 lines
2.7 KiB
C++
105 lines
2.7 KiB
C++
#include "../../gpt4all-backend/llmodel_c.h"
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#include "../../gpt4all-backend/llmodel.h"
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#include "../../gpt4all-backend/llmodel_c.cpp"
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#include "binding.h"
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#include <cassert>
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#include <cmath>
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#include <cstddef>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <string>
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#include <vector>
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#include <iostream>
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#include <unistd.h>
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void* load_model(const char *fname, int n_threads) {
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// load the model
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llmodel_error new_error{};
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auto model = llmodel_model_create2(fname, "auto", &new_error);
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if (model == nullptr ){
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fprintf(stderr, "%s: error '%s'\n",
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__func__, new_error.message);
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return nullptr;
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}
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if (!llmodel_loadModel(model, fname)) {
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return nullptr;
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}
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llmodel_setThreadCount(model, n_threads);
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return model;
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}
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std::string res = "";
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void * mm;
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void model_prompt( const char *prompt, void *m, char* result, int repeat_last_n, float repeat_penalty, int n_ctx, int tokens, int top_k,
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float top_p, float temp, int n_batch,float ctx_erase)
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{
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llmodel_model* model = (llmodel_model*) m;
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// std::string res = "";
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auto lambda_prompt = [](int token_id) {
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return true;
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};
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mm=model;
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res="";
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auto lambda_response = [](int token_id, const char *responsechars) {
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res.append((char*)responsechars);
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return !!getTokenCallback(mm, (char*)responsechars);
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};
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auto lambda_recalculate = [](bool is_recalculating) {
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// You can handle recalculation requests here if needed
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return is_recalculating;
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};
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llmodel_prompt_context* prompt_context = new llmodel_prompt_context{
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.logits = NULL,
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.logits_size = 0,
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.tokens = NULL,
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.tokens_size = 0,
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.n_past = 0,
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.n_ctx = 1024,
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.n_predict = 50,
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.top_k = 10,
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.top_p = 0.9,
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.temp = 1.0,
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.n_batch = 1,
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.repeat_penalty = 1.2,
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.repeat_last_n = 10,
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.context_erase = 0.5
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};
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prompt_context->n_predict = tokens;
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prompt_context->repeat_last_n = repeat_last_n;
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prompt_context->repeat_penalty = repeat_penalty;
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prompt_context->n_ctx = n_ctx;
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prompt_context->top_k = top_k;
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prompt_context->context_erase = ctx_erase;
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prompt_context->top_p = top_p;
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prompt_context->temp = temp;
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prompt_context->n_batch = n_batch;
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llmodel_prompt(model, prompt,
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lambda_prompt,
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lambda_response,
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lambda_recalculate,
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prompt_context );
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strcpy(result, res.c_str());
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free(prompt_context);
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}
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void free_model(void *state_ptr) {
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llmodel_model* ctx = (llmodel_model*) state_ptr;
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llmodel_model_destroy(*ctx);
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}
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