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: 3,102 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 | # 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
@pytest.mark.runs_on(["cpu", "mps"])
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"},
]
@pytest.mark.runs_on(["cpu", "mps"])
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"},
]
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