A 100M-parameter math/reasoning specialist, SFT'd from tinybrainbot-100m-v3-base on a math-heavy diet (order-of-operations, word problems, chain-of-thought). Beats Supra2-100M-Instruct on 5/7 benchmarks on the official EleutherAI LM-Eval Harness.

Architecture: Llama-compatible, 100.1M params (768/12L/12h·4kv, ctx 1024, vocab 32k).
Chat template: <|user|>\n{msg}\n<|end|>\n<|assistant|>\n

Benchmarks (EleutherAI lm-eval, 0-shot, acc_norm; WinoGrande/MMLU = acc)

Benchmark	This model	Supra2-100M-Instruct
ARC-Easy	53.7	44.4
ARC-Challenge	29.0	24.7
OpenBookQA	32.0	30.4
PIQA	65.7	64.4
WinoGrande	50.9	50.5
HellaSwag	33.3	35.9
MMLU	25.7	25.8

Reproduce these numbers

EleutherAI lm-eval-harness v0.4.12, 0-shot, on the HF repo (not the GGUF — llama.cpp's --multiple-choice path under-reports these tasks):

bash

lm_eval --model hf \
  --model_args pretrained=nkthebass/tinybrainbot-100m-v3-math,dtype=float32 \
  --tasks hellaswag,arc_easy,arc_challenge,openbookqa,winogrande,piqa,mmlu \
  --num_fewshot 0 --batch_size 32

Metrics: acc_norm for HellaSwag / ARC / OpenBookQA / PIQA; acc for WinoGrande & MMLU.

What it does

✅ Order-of-operations: correct — e.g. What is 12 + 7 * 3? → "First: 7×3=21, then 12+21 = 33." (the base and general instruct both get this wrong.)
✅ Reasoning: strongest ARC-Challenge of the family.
⚠️ Word problems: attempts chain-of-thought but multi-step arithmetic is unreliable (100M ceiling).
⚠️ Not conversational — it specialized toward math; use tinybrainbot-100m-v3-instruct to chat.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-100m-v3-math")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-100m-v3-math")
prompt = "<|user|>\nWhat is 12 + 7 * 3?\n<|end|>\n<|assistant|>\n"
ids = tok(prompt, return_tensors="pt").input_ids
print(tok.decode(model.generate(ids, max_new_tokens=60)[0][ids.shape[1]:], skip_special_tokens=True))

GGUF

An F16 GGUF is included (tinybrainbot-100m-v3-math-f16.gguf) for llama.cpp / Ollama / LM Studio, with the add_space_prefix=false + leading-space chat template baked in — order-of-operations works faithfully from the GGUF.

Not safety-tuned.