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Model: NoesisLab/Kai-0.35B-Instruct
Source: Original Platform

README.md

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+---
+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