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 pytest | |
| from llamafactory.eval.template import get_eval_template | |
| def test_eval_template_en(): | |
| support_set = [ | |
| { | |
| "question": "Fewshot question", | |
| "A": "Fewshot1", | |
| "B": "Fewshot2", | |
| "C": "Fewshot3", | |
| "D": "Fewshot4", | |
| "answer": "B", | |
| } | |
| ] | |
| example = { | |
| "question": "Target question", | |
| "A": "Target1", | |
| "B": "Target2", | |
| "C": "Target3", | |
| "D": "Target4", | |
| "answer": "C", | |
| } | |
| template = get_eval_template(name="en") | |
| messages = template.format_example(example, support_set=support_set, subject_name="SubName") | |
| assert messages == [ | |
| { | |
| "role": "user", | |
| "content": ( | |
| "The following are multiple choice questions (with answers) about SubName.\n\n" | |
| "Fewshot question\nA. Fewshot1\nB. Fewshot2\nC. Fewshot3\nD. Fewshot4\nAnswer:" | |
| ), | |
| }, | |
| {"role": "assistant", "content": "B"}, | |
| { | |
| "role": "user", | |
| "content": "Target question\nA. Target1\nB. Target2\nC. Target3\nD. Target4\nAnswer:", | |
| }, | |
| {"role": "assistant", "content": "C"}, | |
| ] | |
| def test_eval_template_zh(): | |
| support_set = [ | |
| { | |
| "question": "示例问题", | |
| "A": "示例答案1", | |
| "B": "示例答案2", | |
| "C": "示例答案3", | |
| "D": "示例答案4", | |
| "answer": "B", | |
| } | |
| ] | |
| example = { | |
| "question": "目标问题", | |
| "A": "目标答案1", | |
| "B": "目标答案2", | |
| "C": "目标答案3", | |
| "D": "目标答案4", | |
| "answer": "C", | |
| } | |
| template = get_eval_template(name="zh") | |
| messages = template.format_example(example, support_set=support_set, subject_name="主题") | |
| assert messages == [ | |
| { | |
| "role": "user", | |
| "content": ( | |
| "以下是中国关于主题考试的单项选择题,请选出其中的正确答案。\n\n" | |
| "示例问题\nA. 示例答案1\nB. 示例答案2\nC. 示例答案3\nD. 示例答案4\n答案:" | |
| ), | |
| }, | |
| {"role": "assistant", "content": "B"}, | |
| { | |
| "role": "user", | |
| "content": "目标问题\nA. 目标答案1\nB. 目标答案2\nC. 目标答案3\nD. 目标答案4\n答案:", | |
| }, | |
| {"role": "assistant", "content": "C"}, | |
| ] | |