Instructions to use LongSafari/hyenadna-large-1m-seqlen-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LongSafari/hyenadna-large-1m-seqlen-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LongSafari/hyenadna-large-1m-seqlen-hf", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LongSafari/hyenadna-large-1m-seqlen-hf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LongSafari/hyenadna-large-1m-seqlen-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LongSafari/hyenadna-large-1m-seqlen-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LongSafari/hyenadna-large-1m-seqlen-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LongSafari/hyenadna-large-1m-seqlen-hf
- SGLang
How to use LongSafari/hyenadna-large-1m-seqlen-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 "LongSafari/hyenadna-large-1m-seqlen-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": "LongSafari/hyenadna-large-1m-seqlen-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 "LongSafari/hyenadna-large-1m-seqlen-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": "LongSafari/hyenadna-large-1m-seqlen-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LongSafari/hyenadna-large-1m-seqlen-hf with Docker Model Runner:
docker model run hf.co/LongSafari/hyenadna-large-1m-seqlen-hf
- Xet hash:
- 4b1c680fe2a602b612e21c9a861848d79ca62f20d04cbca7d75bb9f5da2bd198
- Size of remote file:
- 218 MB
- SHA256:
- deafb53209bfafb314d14bd81108546a34d473c07ed7ab7a355674376b56ad12
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.