Instructions to use gwkrsrch/MambaMia-Vicuna-7B-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gwkrsrch/MambaMia-Vicuna-7B-Mini with Transformers:

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

pipe = pipeline("text-generation", model="gwkrsrch/MambaMia-Vicuna-7B-Mini")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gwkrsrch/MambaMia-Vicuna-7B-Mini", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gwkrsrch/MambaMia-Vicuna-7B-Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gwkrsrch/MambaMia-Vicuna-7B-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gwkrsrch/MambaMia-Vicuna-7B-Mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gwkrsrch/MambaMia-Vicuna-7B-Mini

SGLang

How to use gwkrsrch/MambaMia-Vicuna-7B-Mini 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 "gwkrsrch/MambaMia-Vicuna-7B-Mini" \
    --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": "gwkrsrch/MambaMia-Vicuna-7B-Mini",
		"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 "gwkrsrch/MambaMia-Vicuna-7B-Mini" \
        --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": "gwkrsrch/MambaMia-Vicuna-7B-Mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gwkrsrch/MambaMia-Vicuna-7B-Mini with Docker Model Runner:
```
docker model run hf.co/gwkrsrch/MambaMia-Vicuna-7B-Mini
```

MambaMia-Vicuna-7B-Mini

This is the MambaMia Mini model based on lmsys/vicuna-7b-v1.5, designed for efficient long-form video understanding.

Model Description

MambaMia is a State-Space-Model-based hierarchical compression method for efficient video understanding in Large Multimodal Models (LMMs). It addresses the computational cost and information redundancy challenges in processing long videos.

Key Features:

Hierarchical compression using State-Space Models (SSM)
Gated attention mechanism
Learnable sampling strategy
Significantly reduced memory usage and faster inference compared to existing LMMs

Usage

Please refer to the MambaMia repository for detailed usage instructions.

Citation

If you use this model, please cite:

@misc{kim2025mambamia,
  title={MambaMia: A State-Space-Model-Based Compression for Efficient Video Understanding in Large Multimodal Models},
  author={Geewook Kim and Minjoon Seo},
  year={2025},
  eprint={2506.13564},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2506.13564}
}

License

This model is released under the Llama 2 Community License Agreement, following the base model's license.

Acknowledgements

Base Model: lmsys/vicuna-7b-v1.5
Project: MambaMia (AAAI 2026 Oral)

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Safetensors

Model size

7B params

Tensor type

F32

Model tree for gwkrsrch/MambaMia-Vicuna-7B-Mini

Base model

lmsys/vicuna-7b-v1.5

Finetuned

(65)

this model

Paper for gwkrsrch/MambaMia-Vicuna-7B-Mini

MambaMia: A State-Space-Model-Based Compression for Efficient Video Understanding in Large Multimodal Models

Paper • 2506.13564 • Published Jun 16, 2025