- Libraries
- PEFT
How to use rawsun00001/banking-sms-parser-v9 with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("distilgpt2")
model = PeftModel.from_pretrained(base_model, "rawsun00001/banking-sms-parser-v9") - Transformers
How to use rawsun00001/banking-sms-parser-v9 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="rawsun00001/banking-sms-parser-v9") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rawsun00001/banking-sms-parser-v9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rawsun00001/banking-sms-parser-v9 with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rawsun00001/banking-sms-parser-v9"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "rawsun00001/banking-sms-parser-v9",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/rawsun00001/banking-sms-parser-v9
- SGLang
How to use rawsun00001/banking-sms-parser-v9 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 "rawsun00001/banking-sms-parser-v9" \
--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": "rawsun00001/banking-sms-parser-v9",
"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 "rawsun00001/banking-sms-parser-v9" \
--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": "rawsun00001/banking-sms-parser-v9",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' - Docker Model Runner
How to use rawsun00001/banking-sms-parser-v9 with Docker Model Runner:
docker model run hf.co/rawsun00001/banking-sms-parser-v9
