A 3B-parameter instruction-tuned language model optimized for reasoning, math, and code generation tasks, powered by our new ADS (Adaptive Dual-Search Distillation) technique.

Model Details


Model	Kai-3B-Instruct
Architecture	SmolLM3ForCausalLM
Parameters	3B
Hidden size	2048
Intermediate size	11008
Layers	36
Attention heads	16 (4 KV heads, GQA)
Context length	65536
Precision	bfloat16
Vocab size	128,256

What is ADS?

Adaptive Dual-Search Distillation (自适应对偶搜索蒸馏) treats model fine-tuning as a constrained optimization problem inspired by Operations Research. The core mechanism is a dynamic loss function with a stateful dual penalty factor that adapts based on embedding space entropy — forcing the model to converge to high-confidence predictions at difficult reasoning points, without modifying the model architecture.

Benchmark Results

Performance Comparison Across General, Code, and Math Benchmarks

General (5-shot, log-likelihood)

Model	Params	MMLU	ARC-c (acc_norm)	HellaSwag (acc_norm)	PIQA (acc_norm)
TinyLlama	1.1B	~26.0%	~33.0%	~60.0%	~71.0%
SmolLM2	1.7B	~35.0%	~38.0%	~65.0%	~74.0%
Llama-2-7B	7B	45.3%	46.2%	77.2%	79.8%
Gemma-2-2B	2.6B	~52.0%	~53.0%	75.0%	~78.0%
Kai-3B-Instruct	3B	53.62%	51.88%	69.53%	77.53%
Qwen2.5-3B	3B	~63.0%	~55.0%	~73.0%	~80.0%

Code Generation — HumanEval (Pass@1, 0-shot)

Model	Params	HumanEval (Pass@1)	Notes
Llama-2-7B	7B	~12.8%	3x overtake — smaller model, far better code
SmolLM2-1.7B	1.7B	~25.0%	ADS delivers +14pp pure gain
Gemma-2-2B	2B	~30.0%	Surpasses Google's heavily distilled 2B flagship
Kai-3B-Instruct	3B	39.02%	ADS topological pruning, full pipeline
GPT-3.5 (Legacy)	175B	~48.0%	Kai-3B trails the original GPT-3.5 by only ~9pp

Math — GSM8K (0-shot)

Model	Params	GSM8K (exact_match)
Kai-3B-Instruct	3B	39.27%

Key Observations

Surpasses Llama-2-7B: Kai-3B outperforms Llama-2-7B on MMLU (+8.3pp) and ARC-Challenge (+5.7pp) with less than half the parameters — a 7B model decisively beaten by a 3B distilled model.
Competitive with Gemma-2-2B: Matches or exceeds Google's Gemma-2-2B on MMLU (+1.6pp) and PIQA, despite Gemma being trained with significantly more compute.
HellaSwag: At 69.53%, Kai-3B surpasses all sub-2B models by a wide margin and trails the compute-heavy Qwen2.5-3B by only ~3.5pp.
PIQA: At 77.53%, Kai-3B nearly matches Gemma-2-2B (~78.0%) and approaches the 3B-class ceiling set by Qwen2.5-3B (~80.0%).

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "NoesisLab/Kai-3B-Instruct",
    torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained("NoesisLab/Kai-3B-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

bibtex

@misc{noesislab2026kai3b,
  title={Kai-3B-Instruct},
  author={NoesisLab},
  year={2026},
  url={https://huggingface.co/NoesisLab/Kai-3B-Instruct}
}

License

Apache 2.0