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nkthebass
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nkthebass
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| Benchmark | Chat | Supra2-Instruct |
|---|---|---|
| ARC-Easy | 53.2 | 44.4 |
| ARC-Challenge | 28.2 | 24.7 |
| OpenBookQA | 31.2 | 30.4 |
| WinoGrande | 52.0 | 50.5 |
| PIQA | 65.9 | 64.4 |
| MMLU | 25.2 | 25.8 |
| HellaSwag | 32.8 | 35.9 |
Beats Supra2-Instruct on 5/7 — and it holds a conversation.
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-chat,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-chat")model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-100m-v3-chat")prompt = "<|user|>\nHi! How's it going?\n<|end|>\n<|assistant|>\n"ids = tok(prompt, return_tensors="pt").input_idsprint(tok.decode(model.generate(ids, max_new_tokens=60)[0][ids.shape[1]:], skip_special_tokens=True))
An F16 GGUF is included for llama.cpp / Ollama / LM Studio (chat-faithful tokenization).
This is a 100M conversational demo, not a knowledge source. It's genuinely fun to talk to — coherent multi-turn chat with working memory for its size — but it confabulates freely and is not factually reliable (it will confidently make things up). Use it for fun, research, and studying small-model behavior — not for facts, advice you'd act on, or anything safety-relevant. Not aligned or safety-tuned. For raw completion use the base; for arithmetic use math.