Instructions to use jonathanjordan21/mos-mamba-6x130m-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonathanjordan21/mos-mamba-6x130m-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jonathanjordan21/mos-mamba-6x130m-hf", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("jonathanjordan21/mos-mamba-6x130m-hf", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jonathanjordan21/mos-mamba-6x130m-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jonathanjordan21/mos-mamba-6x130m-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jonathanjordan21/mos-mamba-6x130m-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jonathanjordan21/mos-mamba-6x130m-hf
- SGLang
How to use jonathanjordan21/mos-mamba-6x130m-hf 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 "jonathanjordan21/mos-mamba-6x130m-hf" \ --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": "jonathanjordan21/mos-mamba-6x130m-hf", "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 "jonathanjordan21/mos-mamba-6x130m-hf" \ --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": "jonathanjordan21/mos-mamba-6x130m-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jonathanjordan21/mos-mamba-6x130m-hf with Docker Model Runner:
docker model run hf.co/jonathanjordan21/mos-mamba-6x130m-hf
- Xet hash:
- 7945a674d6ddd2c54f63e63bf1960d11c5464a863f47c63d4ca76bae5b2456b1
- Size of remote file:
- 576 MB
- SHA256:
- ed791e9ba38889f46e5b0fbaa3bdbd9243404567176f369073f7ebaf5b5ddba8
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