Public
Star 历史趋势
数据来源: GitHub API · 生成自 Stargazers.cn
README.md
MicroGPT-C
The most atomic way to train and inference a GPT in pure, dependency-free C.
Compilation and Running
Compile with optimizations:
gcc -O3 -march=native -ffast-math -o microgpt microgpt.c -lmFlags explained:
-O3: Maximum optimization-march=native: Use CPU-specific instructions (AVX, etc.)-ffast-math: Faster floating point (trades some precision)-lm: Link math library (for sqrt, exp, etc.)
Run:
./microgptYou should see loss decreasing, then generated samples!
step 1 / 20000 | loss 3.3178 (avg 3.3002)
step 5000 / 20000 | loss 2.6062 (avg 2.2945)
step 10000 / 20000 | loss 1.9652 (avg 2.2562)
step 15000 / 20000 | loss 2.6978 (avg 2.2153)
step 20000 / 20000 | loss 2.3462 (avg 2.2188)
--- inference ---
sample 1: kayley
sample 2: maria
sample 3: arana
sample 4: shayan
sample 5: jayden
sample 6: arian
sample 7: saria
sample 8: amari
sample 9: alila
sample 10: mailyn
c fp32+AVX2 2,631,689 tok/sec