Note that these are Python-only results, the model will not do as well with other languages.
I'm glad to see more domain-focused SLMs, we need more of them! A programming focused MoE should work well across many languages.
I'm glad to see more domain-focused SLMs, we need more of them! A programming focused MoE should work well across many languages.
It is a cheap specialist for closed-world, verifiable reasoning tasks like math, self-contained coding problems, and similar.
"Closed-world" means the needed information is already in the context. It is not a tool-using agent that can discover missing context. "Verifiable" means answers are hard to generate but easy to check.
So no open ended research, repo wide agent work, factual Q&A, or SVG generation. More of a compact reasoning module for bounded problems.
Solve the following first-order ODE for f(x):
((-1 - 2*x)*f(x)*tan(1 + x - exp(-61 - 2*x)*f(x)/x)
+ exp(61 + 2*x)*x*(1 - x*tan(1 + x - exp(-61 - 2*x)*f(x)/x))
+ x*tan(1 + x - exp(-61 - 2*x)*f(x)/x)*f'(x)) = 0
Find the general solution f(x).
f(x) = x*exp(61 + 2*x)*(1 + x - arccos(C/x))
C is an arbitrary constant.
I just tried the quantized Q4_K_M from [1] in my RTX 2070 Super, it ran at 110 tok/s with 1800 tok/s prefill, and found the same solution to your prompt. It generated valid LaTeX for the answer but its reasoning trace uses mostly compact ASCII math notation. Took 3min 22s to answer, spending 22k tokens almost all on thinking.
[1] https://huggingface.co/prithivMLmods/VibeThinker-3B-GGUF
The answer is exactly what you have posted. I am impressed by Qwen!