Instructions to use teilers/mamba-constrict-l2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use teilers/mamba-constrict-l2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="teilers/mamba-constrict-l2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("teilers/mamba-constrict-l2") model = AutoModel.from_pretrained("teilers/mamba-constrict-l2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use teilers/mamba-constrict-l2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "teilers/mamba-constrict-l2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "teilers/mamba-constrict-l2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/teilers/mamba-constrict-l2
- SGLang
How to use teilers/mamba-constrict-l2 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 "teilers/mamba-constrict-l2" \ --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": "teilers/mamba-constrict-l2", "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 "teilers/mamba-constrict-l2" \ --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": "teilers/mamba-constrict-l2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use teilers/mamba-constrict-l2 with Docker Model Runner:
docker model run hf.co/teilers/mamba-constrict-l2
| library_name: transformers | |
| datasets: | |
| - monology/pile-uncopyrighted | |
| base_model: | |
| - state-spaces/mamba-2.8b-hf | |
| pipeline_tag: text-generation | |
| # MambaConstrict | |
| Best performing MambaConstrict model trained using $\ell_2$-norm regularization. | |
| For inquiring, please contact teilers@student.ethz.ch | |