NoesisLab Privacy Policy for OpenRouter Integration
- Data Processing: NoesisLab processes user prompts solely for the purpose of generating model inferences. We do not use any data transmitted through OpenRouter to train or fine-tune our models without explicit user consent.
- Data Retention: We do not store personally identifiable information. Logs are kept for a maximum of 7 days only for debugging and ensuring service stability, after which they are permanently deleted.
- Security: We implement industry-standard encryption to protect data in transit between OpenRouter and our inference endpoints.
- Third Parties: We never sell or share user data with third-party organizations.
Kai-30B-Instruct
A 30B-parameter instruction-tuned language model optimized for reasoning, math, and code generation tasks, powered by our ADS (Adaptive Dual-Search Distillation) technique. The largest model in the Kai family.
Model Details
| Model | Kai-30B-Instruct |
| Architecture | Qwen2ForCausalLM |
| Parameters | ~30B |
| Hidden size | 5120 |
| Intermediate size | 27648 |
| Layers | 64 |
| Attention heads | 40 (8 KV heads, GQA) |
| Context length | 32768 |
| Precision | bfloat16 |
| Vocab size | 152064 |
| Chat template | ChatML (<|im_start|> / <|im_end|>) |
Benchmark Results (5-shot, acc_norm)
| Benchmark | Kai-30B-Instruct | Llama-3 70B | Qwen2.5 32B | Yi-34B | Llama-3 8B | Mistral 7B | Llama-2 7B |
|---|---|---|---|---|---|---|---|
| ARC-C | 64.0 | 83.0 | 70.5 | 65.3 | 60.1 | 55.5 | 53.0 |
| HellaSwag | 74.4 | 89.0 | 85.2 | 83.1 | 78.6 | 81.3 | 78.6 |
| PIQA | 84.8 | 85.0 | 84.1 | 82.5 | 79.8 | 82.1 | 78.1 |
| Winogrande | 86.4 | 83.0 | 78.2 | 76.4 | 73.0 | 74.0 | 69.1 |
What is ADS?
Adaptive Dual-Search Distillation 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.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"NoesisLab/Kai-30B-Instruct",
torch_dtype=torch.bfloat16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("NoesisLab/Kai-30B-Instruct")
messages = [{"role": "user", "content": "What is 25 * 4?"}]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
output = model.generate(
input_ids,
max_new_tokens=512,
temperature=0.6,
top_p=0.8,
do_sample=True,
)
print(tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True))
Citation
@misc{noesislab2026kai30b,
title={Kai-30B-Instruct},
author={NoesisLab},
year={2026},
url={https://huggingface.co/NoesisLab/Kai-30B-Instruct}
}
License
Apache 2.0