---
library_name: transformers
license: apache-2.0
tags:
  - math
  - reasoning
  - text-generation
  - ads
  - distillation
language:
  - en
pipeline_tag: text-generation
model-index:
  - name: Kai-3B-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: 51.88
            name: Accuracy (normalized)
      - task:
          type: multiple-choice
          name: HellaSwag
        dataset:
          name: HellaSwag
          type: Rowan/hellaswag
          split: validation
        metrics:
          - type: acc_norm
            value: 69.53
            name: Accuracy (normalized)
      - task:
          type: multiple-choice
          name: MMLU
        dataset:
          name: MMLU
          type: cais/mmlu
          split: test
        metrics:
          - type: acc
            value: 53.62
            name: Accuracy
      - task:
          type: multiple-choice
          name: PIQA
        dataset:
          name: PIQA
          type: piqa
          split: validation
        metrics:
          - type: acc_norm
            value: 77.53
            name: Accuracy (normalized)
      - task:
          type: text-generation
          name: HumanEval
        dataset:
          name: HumanEval
          type: openai/openai_humaneval
          split: test
        metrics:
          - type: pass@1
            value: 39.02
            name: Pass@1
      - task:
          type: text-generation
          name: GSM8K
        dataset:
          name: GSM8K
          type: gsm8k
          split: test
        metrics:
          - type: exact_match
            value: 39.27
            name: Exact Match (flexible)
---
# Kai-3B-Instruct

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](model_comparison.png)

### 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

1. **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.

2. **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.

3. **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.

4. **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