GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
nkthebass
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
nkthebass
Model Tree
Input Modalities
Output Modalities
Supported Functionality
| Benchmark | This model | Supra2-100M-Base | Supra2-100M-Instruct |
|---|---|---|---|
| ARC-Easy | 54.7 | 47.8 | 44.4 |
| ARC-Challenge | 30.1 | 24.8 | 24.7 |
| OpenBookQA | 34.0 | 32.0 | 30.4 |
| WinoGrande | 53.0 | 50.7 | 50.5 |
| PIQA | 66.2 | 65.5 | 64.4 |
| MMLU | 25.0 | 23.3 | 25.8 |
| HellaSwag | 32.6 | 36.0 | 35.9 |
6/7 vs Supra2-Base and 5/7 vs Supra2-Instruct. HellaSwag is the one benchmark where Supra leads.
EleutherAI lm-eval-harness v0.4.12, 0-shot, evaluated on the HF repo (do not eval the GGUF — llama.cpp's --multiple-choice path under-reports these tasks; the .hellaswag path is fine):
bash
lm_eval --model hf \--model_args pretrained=nkthebass/tinybrainbot-100m-v3-base,dtype=float32 \--tasks hellaswag,arc_easy,arc_challenge,openbookqa,winogrande,piqa,mmlu \--num_fewshot 0 --batch_size 32
Metrics: acc_norm for HellaSwag / ARC-Easy / ARC-Challenge / OpenBookQA / PIQA; acc for WinoGrande & MMLU.
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-100m-v3-base")model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-100m-v3-base")ids = tok("The capital of France is", return_tensors="pt").input_idsprint(tok.decode(model.generate(ids, max_new_tokens=20)[0]))
An F16 GGUF is included (tinybrainbot-100m-v3-base-f16.gguf) for llama.cpp / Ollama / LM Studio, with the correct add_space_prefix=false + leading-space template baked in for faithful tokenization.
A 100M base model: strong on multiple-choice reasoning for its size, but open-ended generation is limited and can be factually unreliable. For chat use the instruct variant; for arithmetic use the math variant. Not aligned or safety-tuned.