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nkthebass
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nkthebass
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| Benchmark | This model | Supra2-100M-Instruct | Δ |
|---|---|---|---|
| ARC-Easy | 54.5 | 44.4 | +10.1 |
| ARC-Challenge | 29.2 | 24.7 | +4.5 |
| OpenBookQA | 32.8 | 30.4 | +2.4 |
| PIQA | 65.5 | 64.4 | +1.1 |
| WinoGrande | 51.4 | 50.5 | +0.9 |
| MMLU | 26.1 | 25.8 | +0.3 |
| HellaSwag | 32.8 | 35.9 | −3.1 |
6/7 vs Supra2-Instruct — only HellaSwag lost.
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-instruct,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.
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-100m-v3-instruct")model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-100m-v3-instruct")prompt = "<|user|>\nWhat is the capital of France?\n<|end|>\n<|assistant|>\n"ids = tok(prompt, return_tensors="pt").input_idsprint(tok.decode(model.generate(ids, max_new_tokens=40)[0][ids.shape[1]:], skip_special_tokens=True))# -> "Paris."
An F16 GGUF is included (tinybrainbot-100m-v3-instruct-f16.gguf) for llama.cpp / Ollama / LM Studio, with the add_space_prefix=false + leading-space chat template baked in so it chats faithfully out of the box.
Coherent for short chat and 1–2 sentence answers; longer generations can confabulate (the 100M ceiling on factual generation). Arithmetic is unreliable — use tinybrainbot-100m-v3-math. Not safety-tuned.