ArmanNN
A hybrid language model combining Causal Attention, Selective SSM (parallel scan), Sparse Mixture-of-Experts, with learned fusion gates and path routers.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("mhd-rahman/ArmanNN-Base", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("mhd-rahman/ArmanNN-Base")
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))