Instructions to use LINs-lab/DynMoE-StableLM-1.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LINs-lab/DynMoE-StableLM-1.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LINs-lab/DynMoE-StableLM-1.6B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LINs-lab/DynMoE-StableLM-1.6B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LINs-lab/DynMoE-StableLM-1.6B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LINs-lab/DynMoE-StableLM-1.6B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LINs-lab/DynMoE-StableLM-1.6B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LINs-lab/DynMoE-StableLM-1.6B
- SGLang
How to use LINs-lab/DynMoE-StableLM-1.6B 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 "LINs-lab/DynMoE-StableLM-1.6B" \ --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": "LINs-lab/DynMoE-StableLM-1.6B", "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 "LINs-lab/DynMoE-StableLM-1.6B" \ --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": "LINs-lab/DynMoE-StableLM-1.6B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LINs-lab/DynMoE-StableLM-1.6B with Docker Model Runner:
docker model run hf.co/LINs-lab/DynMoE-StableLM-1.6B
Download pytorch_model.bin from LINs-lab/DynMoE-StableLM-1.6B: direct link, hf CLI and curl.
- Browser
- Download file 6.4 GB
-
https://huggingface.co/LINs-lab/DynMoE-StableLM-1.6B/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LINs-lab/DynMoE-StableLM-1.6B/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LINs-lab/DynMoE-StableLM-1.6B/resolve/main/pytorch_model.bin
6.4 GB
- Xet hash:
- 19540d5b59ec2ce3cd4bab4a80b110455e497241dfaf3badfcdbf0fdafe15afc
- Size of remote file:
- 6.4 GB
- SHA256:
- 54e57ffaf93e76dc5352000aee4022c9f65d9df20bf087a9d556d7ea40174aaf
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