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nkthebass
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nkthebass
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Zero-shot, full test sets, acc_norm. The (raw acc) row shows plain accuracy. The Intelligence Index is the Open SLM Leaderboard's; the other models' numbers come from that leaderboard.
| Model | Params | Int Index | HellaSwag | ARC-E | ARC-C | PIQA | ArithMark-3 |
|---|---|---|---|---|---|---|---|
| TinyBrainBot-10M-v2-Base (this model) | 9.90M | 13.76 | 27.75 | 36.87 | 25.17 | 58.65 | 49.50 |
| (raw acc) | 13.17 | 27.26 | 39.65 | 18.34 | 59.25 | 49.50 | |
| Ethoric-10M | 9.97M | 12.94 | 28.75 | 37.58 | 24.66 | 59.85 | 41.60 |
| min-spark-1.7 | 9.98M | 12.71 | 29.65 | 39.90 | 24.15 | 60.39 | 36.60 |
| ForgePlex-M2.2-10M | 9.99M | 12.12 | 29.19 | 39.77 | 22.70 | 59.79 | 37.40 |
| heron1i-flash-lite-v2 | 9.92M | 11.11 | 28.86 | 38.89 | 23.38 | 58.54 | 36.70 |
Arithmetic sets the model never trained on (Gemma and Llama were never used to write training data), all acc_norm:
| Set | Score |
|---|---|
| ArithMark-3 (public) | 49.5 |
| Arithmetic stories written by Gemma (544) | 38.4 |
| Arithmetic stories written by Llama (1,093) | 45.6 |
| 23 arithmetic topics ArithMark-3 lacks, new wordings (1,380) | 39.6 |
| Held-out number formats: large / commas / money | 56.2 / 63.9 / 60.6 |
| Bare equations from ArithMark-3 ("43 + 19 =") | 49.1 |
| ArithMark-2 | 34.4 |
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-10m-v2-base")model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-10m-v2-base")ids = tok("The best way to keep bread fresh is to", return_tensors="pt")print(tok.decode(model.generate(**ids, max_new_tokens=24, do_sample=False)[0]))GGUF files for llama.cpp, LM Studio and Jan: tinybrainbot-10m-v2-base-F16.gguf (20 MB) and tinybrainbot-10m-v2-base-Q8_0.gguf (11 MB).
This is a base model: it continues text and does not chat. At this size expect short, simple text.
| Parameters | 9,902,336 total (8.85M non-embedding + 1.05M tied embedding) |
| Shape | hidden 256, 12 layers, 4 query heads of 64 with 2 KV heads (GQA), SwiGLU 704, RMSNorm, RoPE |
| Vocabulary | 4,096-token byte-level BPE: digits split one per token, whitespace kept exactly |
| Context | 1,024 tokens |
10.16B tokens: 8.57B of pretraining at constant learning rate, then a 1.59B-token decay.
| Pretraining (8.57B) | Share |
|---|---|
| DCLM-Ultra (filtered, deduplicated DCLM) | 25% |
| Cosmopedia (Stanford, web samples, WikiHow, OpenStax, Khan Academy) | 24.6% |
| Q&A and facts (our distilled plain-text sets) | 15% |
| FineWeb-Edu | 10% |
| Arithmetic (our drills, Orca-Math, UltraData-Math) | 9% |
| TinyStories | 8% |
| Commonsense, how-to and explanatory writing (our distilled sets) | 4.4% |
| Wikipedia leads | 4% |
| Decay (1.59B) | Share |
|---|---|
| Q&A and facts | 25.5% |
| Cosmopedia | 22.9% |
| DCLM-Ultra-1.1 (a stricter cut of DCLM-Ultra) | 15% |
| Commonsense, how-to and writing | 12.8% |
| Arithmetic | 7.6% |
| TinyStories | 7.3% |
| Wikipedia leads | 5% |
| FineWeb-Edu | 4% |
The arithmetic comes from several sources so the skill is not tied to one wording: drills generated by our own programs (number formats, number words, whitespace variants, negatives, order of operations, tables, unknown positions, distractors; every answer computed in Python), Orca-Math and UltraData-Math word problems, and a very small amount of story problems in LLM-written sentence frames. No benchmark training or test data was used.
Recipe: AdamW (betas 0.9/0.95, weight decay 0.1), peak LR 3e-3 with 1,000 warmup steps, warmup-stable-decay schedule, 131k tokens per step, fp16 mixed precision, trained on 2x V100 in ~8 hours.
How this checkpoint was chosen. Several decay branches with different arithmetic shares were run from the same checkpoint (step 65,303); this is one of them, and the published weights are that branch's final checkpoint, not a merge.
Float32, lm-eval 0.4.13 (full sets, 0-shot) and the official ArithMark-3 script:
text
lm_eval --model hf --model_args pretrained=nkthebass/tinybrainbot-10m-v2-base,dtype=float32 --tasks hellaswag,arc_easy,arc_challenge,piqa --num_fewshot 0python bencharithmark-3.py --model nkthebass/tinybrainbot-10m-v2-base --dtype float32At 10M parameters this is a toy-scale model. It knows few facts, loses coherence after a few sentences, and its arithmetic is unreliable beyond simple problems. HellaSwag and ARC are close to chance for every model this size.
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-10m-v2-base")model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-10m-v2-base")ids = tok("The best way to keep bread fresh is to", return_tensors="pt")print(tok.decode(model.generate(**ids, max_new_tokens=24, do_sample=False)[0]))lm_eval --model hf --model_args pretrained=nkthebass/tinybrainbot-10m-v2-base,dtype=float32 --tasks hellaswag,arc_easy,arc_challenge,piqa --num_fewshot 0python bencharithmark-3.py --model nkthebass/tinybrainbot-10m-v2-base --dtype float32