metadata
library_name: transformers
license: apache-2.0
tags:
- math
- reasoning
- text-generation
language:
- en
pipeline_tag: text-generation
model-index:
- name: Kai-0.35B-Instruct
results:
- task:
type: multiple-choice
name: ARC-Challenge
dataset:
name: ARC-Challenge
type: allenai/ai2_arc
config: ARC-Challenge
split: test
metrics:
- type: acc_norm
value: 37.8
name: Accuracy (normalized)
- task:
type: multiple-choice
name: HellaSwag
dataset:
name: HellaSwag
type: Rowan/hellaswag
split: validation
metrics:
- type: acc_norm
value: 55.88
name: Accuracy (normalized)
- task:
type: multiple-choice
name: PIQA
dataset:
name: PIQA
type: piqa
split: validation
metrics:
- type: acc_norm
value: 71.82
name: Accuracy (normalized)
- task:
type: text-generation
name: MBPP
dataset:
name: MBPP
type: google-research-datasets/mbpp
split: test
metrics:
- type: pass_at_1
value: 22.2
name: pass@1
Kai-0.35B-Instruct
A compact 0.35B-parameter instruction-tuned language model optimized for reasoning, math, and code generation tasks.
Model Details
| Model | Kai-0.35B-Instruct |
| Architecture | LlamaForCausalLM |
| Parameters | 360M |
| Hidden size | 960 |
| Layers | 32 |
| Attention heads | 15 (5 KV heads, GQA) |
| Context length | 8192 |
| Precision | bfloat16 |
| Vocab size | 49,152 |
Benchmark Results (5-shot, log-likelihood)
| Benchmark | Kai-0.35B-Instruct | Mamba (370M) | TinyLlama (1.1B) | Llama-3.2 (1B) |
|---|---|---|---|---|
| ARC-Challenge (science reasoning) | 37.80% | ~29.1% | ~30.1% | ~44.5% |
| HellaSwag (sentence completion) | 55.88% | ~53.8% | ~59.2% | ~61.1% |
| PIQA (physical commonsense) | 71.82% | ~69.6% | ~73.0% | ~74.5% |
Code Generation — MBPP (3-shot, pass@1)
| Model | Params | MBPP pass@1 |
|---|---|---|
| Mamba / Mamba-2 | 370M | <10.0% |
| TinyLlama | 1.1B | ~19.91% |
| Kai-0.35B-Instruct | 360M | 22.20% |
| Llama-3.2-1B (Base) | 1.0B | ~25-30% |
| Llama-3.2-1B-Instruct | 1.0B | ~49.0% |
Key Observations
ARC-Challenge: Kai-0.35B scores 37.80% (5-shot), significantly outperforming both Mamba-370M (+8.7pp) and TinyLlama-1.1B (+7.7pp) — a model 3x its size.
PIQA: At 71.82%, Kai-0.35B nearly matches TinyLlama-1.1B (73.0%) with only 1/3 the parameters, and trails the 1B-class Llama-3.2 by less than 3pp.
MBPP: At 22.20% pass@1, Kai-0.35B surpasses TinyLlama-1.1B (~19.91%) in code generation despite being 3x smaller.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"NoesisLab/Kai-0.35B-Instruct",
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained("NoesisLab/Kai-0.35B-Instruct")
messages = [{"role": "user", "content": "What is 25 * 4?"}]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
output = model.generate(input_ids, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Citation
@misc{noesislab2026nkai,
title={Kai-0.35B-Instruct},
author={NoesisLab},
year={2026},
url={https://huggingface.co/NoesisLab/Kai-0.35B-Instruct}
}
License
Apache 2.0