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
| # Copyright 2025 the LlamaFactory team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import os | |
| import platform | |
| from ..extras.misc import fix_proxy, is_env_enabled | |
| from ..extras.packages import is_gradio_available | |
| from .common import save_config | |
| from .components import ( | |
| create_chat_box, | |
| create_eval_tab, | |
| create_export_tab, | |
| create_footer, | |
| create_infer_tab, | |
| create_top, | |
| create_train_tab, | |
| ) | |
| from .css import CSS | |
| from .engine import Engine | |
| if is_gradio_available(): | |
| import gradio as gr | |
| def create_ui(demo_mode: bool = False) -> "gr.Blocks": | |
| engine = Engine(demo_mode=demo_mode, pure_chat=False) | |
| hostname = os.getenv("HOSTNAME", os.getenv("COMPUTERNAME", platform.node())).split(".")[0] | |
| with gr.Blocks(title=f"LLaMA Factory ({hostname})", css=CSS) as demo: | |
| title = gr.HTML() | |
| subtitle = gr.HTML() | |
| if demo_mode: | |
| gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") | |
| engine.manager.add_elems("head", {"title": title, "subtitle": subtitle}) | |
| engine.manager.add_elems("top", create_top()) | |
| lang: gr.Dropdown = engine.manager.get_elem_by_id("top.lang") | |
| with gr.Tab("Train"): | |
| engine.manager.add_elems("train", create_train_tab(engine)) | |
| with gr.Tab("Evaluate & Predict"): | |
| engine.manager.add_elems("eval", create_eval_tab(engine)) | |
| with gr.Tab("Chat"): | |
| engine.manager.add_elems("infer", create_infer_tab(engine)) | |
| if not demo_mode: | |
| with gr.Tab("Export"): | |
| engine.manager.add_elems("export", create_export_tab(engine)) | |
| engine.manager.add_elems("footer", create_footer()) | |
| demo.load(engine.resume, outputs=engine.manager.get_elem_list(), concurrency_limit=None) | |
| lang.change(engine.change_lang, [lang], engine.manager.get_elem_list(), queue=False) | |
| lang.input(save_config, inputs=[lang], queue=False) | |
| return demo | |
| def create_web_demo() -> "gr.Blocks": | |
| engine = Engine(pure_chat=True) | |
| hostname = os.getenv("HOSTNAME", os.getenv("COMPUTERNAME", platform.node())).split(".")[0] | |
| with gr.Blocks(title=f"LLaMA Factory Web Demo ({hostname})", css=CSS) as demo: | |
| lang = gr.Dropdown(choices=["en", "ru", "zh", "ko", "ja"], scale=1) | |
| engine.manager.add_elems("top", dict(lang=lang)) | |
| _, _, chat_elems = create_chat_box(engine, visible=True) | |
| engine.manager.add_elems("infer", chat_elems) | |
| demo.load(engine.resume, outputs=engine.manager.get_elem_list(), concurrency_limit=None) | |
| lang.change(engine.change_lang, [lang], engine.manager.get_elem_list(), queue=False) | |
| lang.input(save_config, inputs=[lang], queue=False) | |
| return demo | |
| def run_web_ui() -> None: | |
| gradio_ipv6 = is_env_enabled("GRADIO_IPV6") | |
| gradio_share = is_env_enabled("GRADIO_SHARE") | |
| server_name = os.getenv("GRADIO_SERVER_NAME", "[::]" if gradio_ipv6 else "0.0.0.0") | |
| print("Visit http://ip:port for Web UI, e.g., http://127.0.0.1:7860") | |
| fix_proxy(ipv6_enabled=gradio_ipv6) | |
| create_ui().queue().launch(share=gradio_share, server_name=server_name, inbrowser=True) | |
| def run_web_demo() -> None: | |
| gradio_ipv6 = is_env_enabled("GRADIO_IPV6") | |
| gradio_share = is_env_enabled("GRADIO_SHARE") | |
| server_name = os.getenv("GRADIO_SERVER_NAME", "[::]" if gradio_ipv6 else "0.0.0.0") | |
| print("Visit http://ip:port for Web UI, e.g., http://127.0.0.1:7860") | |
| fix_proxy(ipv6_enabled=gradio_ipv6) | |
| create_web_demo().queue().launch(share=gradio_share, server_name=server_name, inbrowser=True) | |