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#include <stdio.h>
#include <stdbool.h>
#include <err.h>
#include <unistd.h>
#include "llama.h"
#include "mem.h"
#define MODEL_PATH "Qwen2.5-7B-Instruct-Q4_K_M.gguf"
#define SYSTEM_PROMPT \
"You are a precise lexicographer. You are defining the EXACT target word provided.\n" \
"Do NOT confuse the word with phonetically or visually similar words.\n\n" \
"Output format strictly as follows:\n" \
"<WORD IN UPPERCASE> (<part of speech>) — <Formality: Conversational|Formal|Literary|Archaic>\n\n" \
"DEFINITION:\n" \
"<terse, highly accurate definition>\n\n" \
"WHEN TO USE:\n" \
"<1 sentence on when to use>\n\n" \
"EXAMPLES:\n" \
"1. <example 1>\n" \
"2. <example 2>\n\n" \
"Do not include intro, markdown, or extra commentary."
#define CHATML_FMT \
"<|im_start|>system\n%s<|im_end|>\n" \
"<|im_start|>user\nTarget word: %s\nDefine this specific word:<|im_end|>\n" \
"<|im_start|>assistant\n"
#define MAX_TOKENS 300
static int build_prompt(char **buf, const char *word)
{
char *p;
int len, written;
if (!buf || !word)
return -1;
len = snprintf(NULL, 0, CHATML_FMT, SYSTEM_PROMPT, word);
if (len <= 0)
return -1;
p = MALLOC((size_t)len + 1);
written = snprintf(p, (size_t)len + 1, CHATML_FMT, SYSTEM_PROMPT, word);
if (written <= 0) {
free(p);
return -1;
}
*buf = p;
return written;
}
static void process_request(struct llama_model *model, const char *word)
{
char *prompt;
int prompt_len;
int n_prompt_tokens, i;
struct llama_context_params cparams;
struct llama_context *ctx;
llama_token *prompt_tokens;
struct llama_batch batch;
struct llama_sampler_chain_params sparams;
struct llama_sampler *smpl;
llama_token new_token_id;
prompt = NULL;
prompt_len = build_prompt(&prompt, word);
if (prompt_len <= 0) {
fprintf(stderr, "Error: failed to construct prompt for '%s'\n", word);
return;
}
cparams = llama_context_default_params();
cparams.n_ctx = 512; /* keep context small to keep RAM usage low */
cparams.n_threads = 4; /* todo: tune */
ctx = llama_init_from_model(model, cparams);
if (!ctx) {
fprintf(stderr, "Error: failed to create context\n");
free(prompt);
return;
}
const struct llama_vocab *vocab = llama_model_get_vocab(model);
n_prompt_tokens = -llama_tokenize(vocab, prompt, prompt_len, NULL,
0, true, true);
if (n_prompt_tokens <= 0) {
fprintf(stderr, "Error: tokenization sizing failed\n");
llama_free(ctx);
free(prompt);
return;
}
prompt_tokens = MALLOC((size_t)n_prompt_tokens * sizeof(llama_token));
if (llama_tokenize(vocab, prompt, prompt_len, prompt_tokens,
n_prompt_tokens, true, true) < 0) {
fprintf(stderr, "Error: Tokenization execution failed\n");
free(prompt_tokens);
llama_free(ctx);
free(prompt);
return;
}
free(prompt); /* no longer required */
batch = llama_batch_get_one(prompt_tokens, n_prompt_tokens);
if (llama_decode(ctx, batch) != 0) {
fprintf(stderr, "Error: Prompt evaluation failed\n");
free(prompt_tokens);
llama_free(ctx);
return;
}
free(prompt_tokens);
/* Generation loop */
sparams = llama_sampler_chain_default_params();
smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_penalties(
64, /* last_n: number of tokens to penalize (64 is standard) */
1.1f, /* repeat_penalty */
0.0f, /* frequency_penalty */
0.0f /* presence_penalty */
));
/* Add Top-P sampling (p = 0.9f, min_keep = 1) */
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9f, 1));
llama_sampler_chain_add(smpl, llama_sampler_init_temp(0.1f)); /* todo: tune */
llama_sampler_chain_add(smpl, llama_sampler_init_dist(LLAMA_DEFAULT_SEED));
for (i = 0; i < MAX_TOKENS; i++) {
new_token_id = llama_sampler_sample(smpl, ctx, -1);
if (llama_vocab_is_eog(vocab, new_token_id))
break;
char buf[128];
int n = llama_token_to_piece(vocab, new_token_id, buf,
sizeof(buf), 0, true);
if (n > 0) {
fwrite(buf, 1, (size_t)n, stdout);
fflush(stdout);
}
batch = llama_batch_get_one(&new_token_id, 1);
if (llama_decode(ctx, batch) != 0)
break;
}
printf("\n");
fflush(stdout);
llama_sampler_free(smpl);
llama_free(ctx);
}
int main(int argc , char *argv[])
{
struct llama_model *model;
struct llama_model_params mparams;
if (unveil(MODEL_PATH, "r") == -1)
err(1, "unveil %s failed", MODEL_PATH);
if (unveil(NULL, NULL) == -1)
err(1, "unveil lock failed");
if (pledge("stdio rpath", NULL) == -1)
err(1, "initial pledge failed");
if (argc < 2)
errx(1, "usage: %s [prompt]", argv[0]);
llama_backend_init();
mparams = llama_model_default_params();
mparams.n_gpu_layers = 0; /* force all layers onto CPU */
mparams.load_mode = LLAMA_LOAD_MODE_MMAP;
model = llama_model_load_from_file(MODEL_PATH, mparams);
if (!model)
errx(1, "failed to load model from file %s", MODEL_PATH);
if (pledge("stdio", NULL) == -1)
err(1, "secondary pledge failed");
const char *word = argv[1];
process_request(model, word);
llama_model_free(model);
llama_backend_free();
return 0;
}
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