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
File size: 4,104 Bytes
83ddd7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # 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)
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