---
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: 37.80
            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: 22.20
            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

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

```python
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

```bibtex
@misc{noesislab2026nkai,
  title={Kai-0.35B-Instruct},
  author={NoesisLab},
  year={2026},
  url={https://huggingface.co/NoesisLab/Kai-0.35B-Instruct}
}
```

## License

Apache 2.0