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README.md
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@@ -99,7 +99,7 @@ A compact 0.35B-parameter instruction-tuned language model optimized for reasoni
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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.
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3. **MBPP**: At **22.20%** pass@1,
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## Usage
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"NoesisLab/Kai-0.35B-Instruct",
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torch_dtype=torch.bfloat16,
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)
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tokenizer = AutoTokenizer.from_pretrained("NoesisLab/
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messages = [{"role": "user", "content": "What is 25 * 4?"}]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
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output = model.generate(input_ids, max_new_tokens=256)
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## Citation
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```bibtex
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@misc{
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title={Kai-0.35B-Instruct},
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author={NoesisLab},
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year={2026},
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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.
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3. **MBPP**: At **22.20%** pass@1, Kai-0.35B surpasses TinyLlama-1.1B (~19.91%) in code generation despite being 3x smaller.
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## Usage
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"NoesisLab/Kai-0.35B-Instruct",
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torch_dtype=torch.bfloat16,
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)
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tokenizer = AutoTokenizer.from_pretrained("NoesisLab/Kai-0.35B-Instruct")
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messages = [{"role": "user", "content": "What is 25 * 4?"}]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
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output = model.generate(input_ids, max_new_tokens=256)
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## Citation
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```bibtex
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@misc{noesislab2026nkai,
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title={Kai-0.35B-Instruct},
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author={NoesisLab},
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year={2026},
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