Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
Paper • 2503.16252 • Published • 32
How to use fenglui/Fin-R1-awq with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fenglui/Fin-R1-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "fenglui/Fin-R1-awq",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/fenglui/Fin-R1-awq
How to use fenglui/Fin-R1-awq with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "fenglui/Fin-R1-awq" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "fenglui/Fin-R1-awq",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "fenglui/Fin-R1-awq" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "fenglui/Fin-R1-awq",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use fenglui/Fin-R1-awq with Docker Model Runner:
docker model run hf.co/fenglui/Fin-R1-awq
Base model
SUFE-AIFLM-Lab/Fin-R1