HyperMambaLM-300M

⚠️ This is an architecture-only repository – no pretrained weights are available yet.

HyperMambaLM is a research prototype combining modern state-space modeling with meta-learning components.
Inspired by Mamba, but extended with additional mechanisms for few-shot adaptation, neuro-symbolic reasoning, and progressive learning.

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🧠 Highlights

• 🌀 Mamba-style SSM: Parallel scan for efficient sequence modeling
• 🧬 Meta-Learning (MAML): Learns to adapt with few examples
• 🧠 Neuro-Symbolic Layer: Combines neural networks with logic reasoning
• 🌱 Progressive & Continual Learning: Learns without forgetting
• 💡 Adaptive Precision: Smart compute control
• 🧩 Built for: NAS, federated learning, knowledge distillation...

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📂 Files included

File	Description
config.json	Model hyperparameters
modeling_hypermamba.py	Core model definition
modeling_utils.py	(Optional) Utility components
demo.py	Quick usage test
__init__.py	Python module loader
README.md	This file

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🚀 Quickstart (Colab / Local)

📌 This model is not yet trained, so only the architecture is available.

# Step 1: Download model code (if not cloned)
!wget https://huggingface.co/hoanghai2110/HyperMambaLM-300M/resolve/main/modeling_hypermamba.py

# Step 2: Import and initialize
from modeling_hypermamba import HyperMambaLM, HyperMambaConfig

config = HyperMambaConfig.from_pretrained("hoanghai2110/HyperMambaLM-300M")
model = HyperMambaLM(config)

# Step 3: Run a dummy forward pass
import torch
input_ids = torch.randint(0, config.vocab_size, (1, 16))
output = model(input_ids)

print("✅ Output shape:", output.logits.shape)  # [1, 16, vocab_size]