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nkthebass
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Model Details
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nkthebass
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nkthebass/tinybrainbot-100m-v3-base
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Output Modalities
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Measured on held-out prompts that were not used in training:
| Behaviour | Result |
|---|---|
| Thinks on everyday decision / advice / "why" questions | 100% |
| Thinks on greetings and simple facts | 0% |
| Declines clearly harmful requests | 87% |
| Declines harmless requests that only sound alarming ("how do I kill mould?") | ~5% |
Refusal figures come from 60 prompts per category, so treat them as indicative.
temperature 0.4, top_p 0.95, repetition_penalty 1.15 — these are already the defaults in
generation_config.json, so a plain model.generate(...) uses them.
Don't raise the temperature much above ~0.7: at 1.0 it declines harmful requests noticeably less often. Keep the repetition penalty on; without it the model can repeat itself.
python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "nkthebass/tinybrainbot-100m-v4-thinking"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo) prompt = "<|user|>\nShould I get a dog or a cat if I live in a small apartment?\n<|end|>\n<|assistant|>\n"ids = tok(prompt, return_tensors="pt").input_idsout = model.generate(ids) # sampling defaults come from generation_config.jsontext = tok.decode(out[0][ids.shape[1]:], skip_special_tokens=False).split("<|end|>")[0] thinking, _, answer = text.rpartition("</think>")thinking = thinking.replace("<think>", "").strip()print(answer.strip()) # the reply, with the reasoning hiddenthinking holds the reasoning if you want to show or log it; for a direct reply it is empty.
An F16 GGUF (tinybrainbot-100m-v4-thinking-f16.gguf, 200 MB) and a Q8_0 GGUF (tinybrainbot-100m-v4-thinking-q8_0.gguf, 107 MB) are included for llama.cpp,
LM Studio and Ollama. The chat template is embedded, and the tokenizer is set up so llama.cpp
tokenizes chat messages exactly as the model was trained.
bash
llama-server -m tinybrainbot-100m-v4-thinking-f16.gguf -c 1024 --temp 0.4 --top-p 0.95 --repeat-penalty 1.15Set the same three values in LM Studio / Ollama — their default temperature (0.8) is higher
than this model should run at. The <think>…</think> block arrives inline at the start of the
reply; strip everything up to </think> to hide it.
EleutherAI lm-eval v0.4.13, 0-shot, acc_norm (WinoGrande and MMLU: acc):
| Benchmark | This model | 100M v3 Instruct | Supra2-100M-Instruct |
|---|---|---|---|
| ARC-Easy | 52.5 | 54.5 | 44.4 |
| ARC-Easy (raw acc) | 56.4 | 58.3 | 51.4 |
| ARC-Challenge | 27.7 | 29.2 | 24.7 |
| ARC-Challenge (raw acc) | 26.3 | 26.7 | 22.7 |
| OpenBookQA | 33.2 | 32.8 | 30.4 |
| OpenBookQA (raw acc) | 20.4 | 21.6 | 17.2 |
| PIQA | 64.9 | 65.5 | 64.4 |
| PIQA (raw acc) | 65.0 | 65.3 | 64.3 |
| WinoGrande | 49.5 | 51.4 | 50.5 |
| MMLU | 25.3 | 26.1 | 25.8 |
| HellaSwag | 33.3 | 32.8 | 35.9 |
| HellaSwag (raw acc) | 30.0 | 30.1 | 31.5 |
Multiple-choice benchmarks score answer likelihoods directly, so the model never gets to think during them — they measure what it knows, not whether its reasoning helps.
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "nkthebass/tinybrainbot-100m-v4-thinking"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo) prompt = "<|user|>\nShould I get a dog or a cat if I live in a small apartment?\n<|end|>\n<|assistant|>\n"ids = tok(prompt, return_tensors="pt").input_idsout = model.generate(ids) # sampling defaults come from generation_config.jsontext = tok.decode(out[0][ids.shape[1]:], skip_special_tokens=False).split("<|end|>")[0] thinking, _, answer = text.rpartition("</think>")thinking = thinking.replace("<think>", "").strip()print(answer.strip()) # the reply, with the reasoning hiddenllama-server -m tinybrainbot-100m-v4-thinking-f16.gguf -c 1024 --temp 0.4 --top-p 0.95 --repeat-penalty 1.15