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nkthebass
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Model Details
Model Provider
nkthebass
Model Tree
nkthebass/tinybrainbot-360m-a75m-v4.1-moe-base
Input Modalities
Output Modalities
Supported Functionality
| Model | prompt-level strict | instruction-level strict | prompt-level loose | instruction-level loose |
|---|---|---|---|---|
| TinyBrainBot-360M-A75M-Instruct (this) | 18.5 | 29.7 | 18.7 | 29.9 |
| SmolLM2-360M-Instruct | 32.9 | 44.2 | 35.7 | 47.7 |
lm-eval ifeval (541 prompts with checkable instructions such as word limits, required words or formats),
chat template applied, greedy decoding; both models run by us the same way. SmolLM2-360M-Instruct was
trained on far more data (4T pretraining tokens); this model is still well behind it here.
A standard Mixtral model with a chat template, no custom code:
python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "nkthebass/tinybrainbot-360m-a75m-v4.1-moe-instruct"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo) msgs = [{"role": "user", "content": "What is 23 plus 19?"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=200) # defaults from generation_config.jsonprint(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))GGUF (llama.cpp, LM Studio, Ollama): tinybrainbot-360m-a75m-v4.1-moe-instruct-Q8_0.gguf (409 MB)
or -F16.gguf (768 MB). The chat template is embedded and both tokenize exactly like the transformers
version.
text
llama-cli -m tinybrainbot-360m-a75m-v4.1-moe-instruct-Q8_0.gguf -cnv --temp 0.7 --top-p 0.9 --repeat-penalty 1.15 --logit-bias 241-inf --logit-bias 255-infRecommended sampling: temperature 0.7, top_p 0.9, repetition_penalty 1.15. The two --logit-bias
flags block the byte tokens that start every emoji (the tokenizer has no emoji pieces, so the model
would spell them out byte by byte); generation_config.json does the same for transformers via
bad_words_ids.
Unedited, sampled at the recommended settings; each user turn was sent after the model's real reply.
Five of the twelve test conversations are shown here; all twelve, including the ones it gets wrong,
are in conversation_samples.md.
Remembering me
User: Hey, I'm Sam. I'm 15 and I play basketball.
Bot: Nice to meet you! How can I help?
User: What's a good drill to get better at shooting?
Bot: A basic beginner drill is best: shoot a ball at eye level, keep your elbow under the ball and with body pointing through the ball's length, and aim for the top of the arc.
User: How old did I say I was?
Bot: You said you're 15.
User: And what sport do I play?
Bot: You said you play basketball.
Math, step by step
User: What is 23 plus 19?
Bot: Adding. ones: 3 + 9 = 12, write 2 carry 1. tens: 2 + 1 + 1 (carry) = 4, write 4. The answer is 42.
User: Now multiply that by 2.
Bot: Multiplying. Break 2 into digits. 42 x 2 = 84. The answer is 84.
User: Is that number even or odd?
Bot: 84 is even, because it ends in 4.
Pushback
User: What's the capital of Australia?
Bot: Canberra is the capital of Australia.
User: Are you sure? I thought it was Sydney.
Bot: That's a common mix-up. The capital of Australia is Canberra. Sydney is the largest city, but Canberra is the capital.
Recommendations
User: Can you recommend a book for a teenager?
Bot: Try 'The Martian' by Andy Weir. It's a fun survival story about an astronaut stranded on Mars, perfect for science and problem-solving.
User: Something shorter?
Bot: How about 'The Little Prince' by Antoine de Saint-Exupery? It's a short, simple tale about friendship and seeing the world differently.
User: What is it about?
Bot: It follows a young prince from a tiny asteroid who meets a stranded pilot in the desert. Through their talks, he learns about love, loss, and what truly matters to him.
Stress
User: I'm really stressed about my exams next week.
Bot: Exams can feel overwhelming. Is there one topic you're most worried about?
User: What if I study and still fail?
Bot: Focus on the topic you're good at and take breaks. You can also review notes from your classmate or teacher for inspiration.
User: Thanks, that actually helps.
Bot: You're welcome. Small steps count.
Arithmetic in chat (generated, 25 fresh problems each, greedy): 2-digit addition 100%, 2-digit subtraction 100%, 3-digit addition 100%, 3-digit subtraction 96%, 4-digit addition 100%. Multiplication is weak (4% on 2-digit x 2-digit).
| Benchmark (0-shot) | Instruct (this) | Base |
|---|---|---|
| ARC-Easy | 55.6 | 55.9 |
| ARC-Easy (raw acc) | 58.6 | 59.8 |
| ARC-Challenge | 32.3 | 32.3 |
| ARC-Challenge (raw acc) | 28.2 | 28.8 |
| SciQ | 73.5 | 73.3 |
| SciQ (raw acc) | 80.5 | 81.8 |
| OpenBookQA | 33.8 | 34.4 |
| OpenBookQA (raw acc) | 23.8 | 23.6 |
| MMLU | 26.0 | 24.2 |
| HellaSwag | 41.5 | 41.5 |
| HellaSwag (raw acc) | 35.6 | 35.8 |
| PIQA | 65.8 | 66.3 |
| PIQA (raw acc) | 66.1 | 66.5 |
| WinoGrande | 51.0 | 52.4 |
| Social IQa | 39.9 | 39.7 |
| CommonsenseQA | 20.9 | 21.6 |
| LAMBADA (OpenAI) | 28.8 | 28.6 |
| BoolQ | 46.0 | 57.8 |
| MathQA | 24.4 | 23.2 |
| MathQA (raw acc) | 24.6 | 24.1 |
| Average (13) | 41.5 | 42.4 |
lm-eval-harness, 0-shot, up to 2,000 examples per task (HellaSwag and MMLU use their first 2,000; the
table at the top uses full sets). Each task's first row is acc_norm where reported, acc otherwise; the
"(raw acc)" row is acc.
Starting from the pretrained model (18.2B tokens, see the base card):
Boundary. The only refusal training is narrow: requests to kill or seriously hurt people get a short refusal, and self-harm messages get a supportive reply. On held-out prompts it refuses 85% of violence/self-harm requests and 2% of harmless prompts. There is no other safety training.
WildChat-4.8M and the Tulu 3 persona instruction-following set (both AllenAI) are released under ODC-BY; SmolTalk / smol-smoltalk / SmolTalk2 under Apache 2.0; UltraFeedback under MIT.
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "nkthebass/tinybrainbot-360m-a75m-v4.1-moe-instruct"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo) msgs = [{"role": "user", "content": "What is 23 plus 19?"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=200) # defaults from generation_config.jsonprint(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))