Instructions to use FinStep/Alpha-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FinStep/Alpha-R1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FinStep/Alpha-R1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FinStep/Alpha-R1")
model = AutoModelForCausalLM.from_pretrained("FinStep/Alpha-R1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FinStep/Alpha-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FinStep/Alpha-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FinStep/Alpha-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FinStep/Alpha-R1

SGLang

How to use FinStep/Alpha-R1 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 "FinStep/Alpha-R1" \
    --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": "FinStep/Alpha-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "FinStep/Alpha-R1" \
        --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": "FinStep/Alpha-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FinStep/Alpha-R1 with Docker Model Runner:
```
docker model run hf.co/FinStep/Alpha-R1
```

iamzuoyou commited on 3 days ago

Commit

b85f0ec

verified ·

1 Parent(s): ba755e1

Update README_en.md: absolute language links, combined results figure

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README_en.md +2 -3

README_en.md CHANGED Viewed

@@ -1,7 +1,7 @@
 # Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning
 <p align="center">
-  <a href="README_en.md">English</a> | <a href="README.md">中文</a>
 </p>
 **Alpha-R1** is a reasoning-enhanced LLM for quantitative alpha selection: built on Qwen3-8B and trained with GRPO reinforcement learning ([verl](https://github.com/volcengine/verl)) using a market-feedback reward. It reasons over **semantic factor descriptions** — how each factor works, when it works, and when it fails — and selects the Alpha101 factors that best fit current market conditions.
@@ -74,8 +74,7 @@ Validation and parsing scripts are provided under `src/alpha_r1/parsing/` in the
 12-month out-of-sample testing (2025-01-01 to 2025-12-31, paper Table 1):
 <p align="center">
-  <img src="assets/main_result_sp500.png" alt="Backtest NAV comparison on the S&P 500 asset pool" style="width: 49%;">
-  <img src="assets/main_result_csi300.png" alt="Backtest NAV comparison on the CSI 300 asset pool" style="width: 49%;">
 </p>
 <table>

 # Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning
 <p align="center">
+  <a href="https://huggingface.co/FinStep/Alpha-R1/blob/main/README_en.md">English</a> | <a href="https://huggingface.co/FinStep/Alpha-R1/blob/main/README.md">中文</a>
 </p>
 **Alpha-R1** is a reasoning-enhanced LLM for quantitative alpha selection: built on Qwen3-8B and trained with GRPO reinforcement learning ([verl](https://github.com/volcengine/verl)) using a market-feedback reward. It reasons over **semantic factor descriptions** — how each factor works, when it works, and when it fails — and selects the Alpha101 factors that best fit current market conditions.
 12-month out-of-sample testing (2025-01-01 to 2025-12-31, paper Table 1):
 <p align="center">
+  <img src="assets/main_results.png" alt="Backtest NAV comparison on S&P 500 (left) and CSI 300 (right)" style="width: 100%;">
 </p>
 <table>