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: 2,792 Bytes
0396b0d | 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 | import tweepy
import datetime
import os
def get_tweets_from_user_list_v2(user_list, client, tweets_per_user=10):
"""
Retrieves the latest tweets from a list of Twitter users within the last 24 hours using Tweepy API v2.
"""
all_tweets = []
now = datetime.datetime.now(datetime.timezone.utc)
yesterday = now - datetime.timedelta(days=1)
for username in user_list:
try:
user = client.get_user(username=username)
if user.errors:
print(f"Error getting user ID for {username}: {user.errors}")
continue
user_id = user.data['id']
response = client.get_users_tweets(
id=user_id,
max_results=tweets_per_user,
tweet_fields=["created_at", "text"],
)
if response.errors:
print(f"Error fetching tweets for {username} (ID: {user_id}): {response.errors}")
continue
tweets = response.data
if tweets:
for tweet in tweets:
tweet_time = tweet['created_at']
if tweet_time >= yesterday:
all_tweets.append(tweet)
else:
break
except Exception as e:
print(f"Unexpected error fetching tweets for {username}: {e}")
continue
return all_tweets
def authenticate_v2():
"""Authenticates with the Twitter API v2 using a Bearer Token."""
bearer_token = (f"AAAAAAAAAAAAAAAAAAAAAKmVzAEAAAAAhVM%2BPoJytUvDoQ%2FgIGfLdJXdocU%3DvS5ZF10O60kLJcY6MGiErCDHV7awcUKB75QqWpIlKh84rK5CFw")
if not bearer_token:
print("Error: Missing Twitter Bearer Token. Please set the environment variable.")
return None
try:
client = tweepy.Client(bearer_token)
user = client.get_me() # Check authentication
if user.errors:
print(f"Authentication check failed: {user.errors}")
return None
print("Authentication successful (API v2)!")
return client
except Exception as e:
print(f"Authentication failed (API v2): {e}")
return None
if __name__ == '__main__':
client = authenticate_v2()
if client is None:
print("Exiting due to authentication failure.")
exit()
user_list = ["elonmusk", "BillGates", "NASA"]
tweets = get_tweets_from_user_list_v2(user_list, client, tweets_per_user=5)
if tweets:
print(f"Found {len(tweets)} tweets from the last 24 hours:")
for tweet in tweets:
print(f"Text: {tweet['text']}\n")
print(f"Tweeted at: {tweet['created_at']}\n")
else:
print("No tweets found in the last 24 hours or an error occurred.") |