Instructions to use hoanghai2110/HyperMambaLM-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hoanghai2110/HyperMambaLM-300M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hoanghai2110/HyperMambaLM-300M", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hoanghai2110/HyperMambaLM-300M", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hoanghai2110/HyperMambaLM-300M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hoanghai2110/HyperMambaLM-300M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hoanghai2110/HyperMambaLM-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hoanghai2110/HyperMambaLM-300M

SGLang

How to use hoanghai2110/HyperMambaLM-300M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "hoanghai2110/HyperMambaLM-300M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hoanghai2110/HyperMambaLM-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "hoanghai2110/HyperMambaLM-300M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hoanghai2110/HyperMambaLM-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hoanghai2110/HyperMambaLM-300M with Docker Model Runner:
```
docker model run hf.co/hoanghai2110/HyperMambaLM-300M
```

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.

🧠 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...

📂 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

🚀 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]

Downloads last month: 18

Paper for hoanghai2110/HyperMambaLM-300M

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Paper • 1703.03400 • Published Mar 9, 2017 • 1