GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
MaliosDark
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
MaliosDark
Model Tree
Base
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
The model reads and writes numbers least-significant-digit first, with a digit-atomic tokenizer.
Aligning carry propagation with reading order is what lets a model this small do arithmetic. The digit
reversal happens inside the tokenizer, so you pass and receive normal text. This requires
trust_remote_code=True.
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("MaliosDark/Nexus-Erebus-3M", trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained("MaliosDark/Nexus-Erebus-3M")
Full test sets, 0-shot. acc_norm for multiple choice, accuracy for ArithMark-2.
| Task | Score |
|---|---|
| ArithMark-2 | 70.72 |
| PIQA | 51.41 |
| HellaSwag | 26.59 |
| ARC-Easy | 27.02 |
| ARC-Challenge | 20.65 |
The model is built for integer arithmetic. Non-arithmetic tasks sit near chance, which is expected at this size.
bash
python benchmark_nexus_arithmark.py MaliosDark/Nexus-Erebus-3M
Pretrained from scratch with a digit-atomic 4k tokenizer, least-significant-digit order, and a large stream of freshly generated synthetic integer arithmetic (addition, subtraction, multiplication, exact division, mixed and parenthesised multi-operator expressions), mixed with TinyStories for language. Then a short fine-tune on public benchmark train splits plus more arithmetic. No evaluation items were used at any stage. Fresh generation, never reusing a fixed pool, is what stops the model from memorising instead of learning.
MIT. Trained from scratch: 100% original weights, own tokenizer, no base model.