Text Generation
Transformers
TensorBoard
Safetensors
biology
genomics
rna
sequence-generation
regression
reinforcement-learning
git-lfs
Instructions to use JoyXiangLab/rnaseek-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoyXiangLab/rnaseek-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JoyXiangLab/rnaseek-full")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JoyXiangLab/rnaseek-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JoyXiangLab/rnaseek-full with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JoyXiangLab/rnaseek-full" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JoyXiangLab/rnaseek-full
- SGLang
How to use JoyXiangLab/rnaseek-full 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 "JoyXiangLab/rnaseek-full" \ --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": "JoyXiangLab/rnaseek-full", "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 "JoyXiangLab/rnaseek-full" \ --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": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JoyXiangLab/rnaseek-full with Docker Model Runner:
docker model run hf.co/JoyXiangLab/rnaseek-full
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channels:
- defaults
- conda-forge
- bioconda
dependencies:
- python=3.11
- pip
- setuptools
- wheel
- packaging
- ninja
- pybind11
- pytorch=2.10.0=gpu_cuda128_py311*
- torchvision=0.25.0=cuda128py311*
- torchaudio=2.10.0=cuda128py311*
- pip:
- flash-attn==2.6.3
- torchcodec==0.10.0
- numpy==2.3.5
- pandas
- scipy==1.16.3
- scikit-learn==1.7.2
- matplotlib==3.10.7
- seaborn==0.13.2
- biopython==1.86
- cutadapt
- multiqc
- pysam==0.23.3
- h5py==3.16.0
- openpyxl==3.1.5
- statsmodels==0.14.6
- numba==0.62.1
- pyarrow==21.0.0
- ipykernel
- ipywidgets==8.1.8
- accelerate==1.13.0
- transformers==5.5.4
- datasets==4.8.4
- peft==0.18.1
- trl==1.0.0
- bitsandbytes==0.48.2
- safetensors==0.5.3
- sentencepiece==0.2.1
- tokenizers==0.22.1
- tensorboard==2.20.0
- fastapi==0.123.10
- uvicorn==0.38.0
- pydantic
- psutil
- requests
- tqdm
- ViennaRNA==2.7.0
- logomaker==0.8.7
- umap-learn==0.5.11
- anndata==0.12.10
- pydeseq2==0.5.4
- zarr==3.1.6
- librosa==0.11.0
- audiomentations==0.43.1
- soundfile==0.13.1
- soxr==0.5.0.post1
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