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How to use Abigail45/Aurora-Fusion with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Abigail45/Aurora-Fusion") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Abigail45/Aurora-Fusion", dtype="auto")How to use Abigail45/Aurora-Fusion with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abigail45/Aurora-Fusion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Abigail45/Aurora-Fusion",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Abigail45/Aurora-Fusion
How to use Abigail45/Aurora-Fusion with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Abigail45/Aurora-Fusion" \
--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": "Abigail45/Aurora-Fusion",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "Abigail45/Aurora-Fusion" \
--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": "Abigail45/Aurora-Fusion",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Abigail45/Aurora-Fusion with Docker Model Runner:
docker model run hf.co/Abigail45/Aurora-Fusion
Aurora-Fusion is a high-performance multilingual causal language model created by merging five state-of-the-art base models. It is optimized for instruction following, reasoning, and chat, and supports English, Spanish, and French natively, while maintaining multilingual capabilities.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("YourUsername/Aurora-Fusion")
model = AutoModelForCausalLM.from_pretrained(
"YourUsername/Aurora-Fusion",
device_map="auto",
load_in_4bit=True # Optional: requires bitsandbytes
)
prompt = "Explain why Axolotls are so cute?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=250,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))