Instructions to use decula/sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use decula/sd with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "decula/sd") - Transformers
How to use decula/sd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="decula/sd")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("decula/sd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use decula/sd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "decula/sd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "decula/sd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/decula/sd
- SGLang
How to use decula/sd 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 "decula/sd" \ --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": "decula/sd", "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 "decula/sd" \ --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": "decula/sd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use decula/sd with Docker Model Runner:
docker model run hf.co/decula/sd
File size: 1,360 Bytes
4925137 | 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 | import os
import pymysql
db_config = {
'host': '192.168.100.188',
'user': 'root',
'password': 'Csiq@2019',
'db': 'porn',
'charset': 'utf8mb4',
'cursorclass': pymysql.cursors.DictCursor
}
# 连接到数据库
directory_path = 'C:\\games\\H\\h-corpus'
# 遍历指定目录下的所有.txt文件
for filename in os.listdir(directory_path):
if filename.endswith('.txt'):
file_path = os.path.join(directory_path, filename)
try:
# 打开数据库连接
connection = pymysql.connect(max_allowed_packet=1024 * 1024 * 64, **db_config)
with connection.cursor() as cursor:
with open(file_path, 'r', encoding='utf-8') as file:
title = file.readline().strip() # 读取标题
content = file.read().strip() # 读取内容
# 插入数据到数据库
sql = "INSERT INTO hnote (title, content) VALUES (%s, %s)"
cursor.execute(sql, (title, content))
# 提交事务
connection.commit()
except Exception as e:
print(f"An error occurred: {e}")
finally:
# 关闭数据库连接
if connection:
connection.close()
print("All .txt files have been processed and inserted into the database.") |