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
| cff-version: 1.2.0 | |
| date-released: 2024-03 | |
| message: "If you use this software, please cite it as below." | |
| authors: | |
| - family-names: "Zheng" | |
| given-names: "Yaowei" | |
| - family-names: "Zhang" | |
| given-names: "Richong" | |
| - family-names: "Zhang" | |
| given-names: "Junhao" | |
| - family-names: "Ye" | |
| given-names: "Yanhan" | |
| - family-names: "Luo" | |
| given-names: "Zheyan" | |
| - family-names: "Feng" | |
| given-names: "Zhangchi" | |
| - family-names: "Ma" | |
| given-names: "Yongqiang" | |
| title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models" | |
| url: "https://arxiv.org/abs/2403.13372" | |
| preferred-citation: | |
| type: conference-paper | |
| conference: | |
| name: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)" | |
| authors: | |
| - family-names: "Zheng" | |
| given-names: "Yaowei" | |
| - family-names: "Zhang" | |
| given-names: "Richong" | |
| - family-names: "Zhang" | |
| given-names: "Junhao" | |
| - family-names: "Ye" | |
| given-names: "Yanhan" | |
| - family-names: "Luo" | |
| given-names: "Zheyan" | |
| - family-names: "Feng" | |
| given-names: "Zhangchi" | |
| - family-names: "Ma" | |
| given-names: "Yongqiang" | |
| title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models" | |
| url: "https://arxiv.org/abs/2403.13372" | |
| year: 2024 | |
| publisher: "Association for Computational Linguistics" | |
| address: "Bangkok, Thailand" | |