Miamnus-M1-Nano-v1.8

Miamnus-M1-Nano-v1.8 is a compact, high-efficiency hybrid causal language model engineered by Miamnus AI. Built on a 3:1 recurring hybrid architecture pairing KDA linear attention with GQA attention layers across 24 transformer blocks.

This release package is completely independent, self-contained, and written in pure PyTorch. It contains ONLY the code required for this model, with zero external Triton or CUDA kernel compilation dependencies. It runs out of the box on Apple Silicon (MPS), CPU, and NVIDIA CUDA.

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Specifications

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Quickstart (Hugging Face Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MiamnusAI/Miamnus-M1-Nano-v1.8"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

inputs = tokenizer("Once upon a time in a sunny valley,", return_tensors="pt")
output = model.generate(inputs["input_ids"], max_new_tokens=60, temperature=0.7)
print(tokenizer.decode(output[0]))

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Local Standalone Inference

python generate.py --prompt "Once upon a time" --max_tokens 80 --temperature 0.75

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Citation

@article{miamnus2026m1nano,
  title={Miamnus M1 Nano: Efficient 3:1 Hybrid Linear Attention Language Models},
  author={Miamnus AI Research Team},
  journal={Miamnus Technical Report Series},
  year={2026},
  url={https://huggingface.co/MiamnusAI/Miamnus-M1-Nano-v1.8}
}