Flexan/NoesisLab-Kai-0.35B-Instruct-GGUF

GGUF Files for Kai-0.35B-Instruct

These are the GGUF files for NoesisLab/Kai-0.35B-Instruct.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.

You can find more info about me and what I do here.

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

1. 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.

2. 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.

3. 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