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
```

Alpha-R1 / README_en.md

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	# Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning

	<p align="center">
	<img src="assets/Alpha-R1.png" alt="Alpha-R1" style="width: 100%; height: auto;">
	</p>

	<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.

	- 📄 Paper: [arXiv:2512.23515](https://arxiv.org/abs/2512.23515)
	- 💻 Code: [FinStep-AI/Alpha-R1](https://github.com/FinStep-AI/Alpha-R1) (inference pipeline / qlib backtesting / training config)
	- 📜 License: MIT

	## Model Overview

	<p align="center">
	<img src="assets/framework.png" alt="Alpha-R1 framework overview" style="width: 100%;">
	</p>

	\| Item \| Content \|
	\|---\|---\|
	\| Base model \| [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) \|
	\| Training \| GRPO (verl) with a market-feedback reward \|
	\| Input \| Decision-context prompt: concatenated semantic factor descriptions `α_des` \|
	\| Output \| Selected factors listed in `<alpha_list>` \|
	\| Candidate pool \| 82 Alpha101 factors (as screened in the paper) \|
	\| Recommended decoding \| temperature=0 (greedy), top_p=0.7, max_new_tokens=4096 \|

	## Quick Start

	### transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "FinStep/Alpha-R1"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

	prompt = "<decision context: concatenated factor descriptions>" # see the GitHub repo for the prompt builder
	inputs = tokenizer.apply_chat_template(
	[{"role": "user", "content": prompt}],
	add_generation_prompt=True, return_tensors="pt",
	).to(model.device)

	# paper setting: temperature=0 (greedy), top_p=0.7
	out = model.generate(inputs, max_new_tokens=4096, do_sample=False)
	print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
	```

	### vLLM

	```python
	from vllm import LLM, SamplingParams

	llm = LLM(model="FinStep/Alpha-R1")
	params = SamplingParams(temperature=0.0, top_p=0.7, max_tokens=4096)
	outputs = llm.chat([[{"role": "user", "content": prompt}]], params)
	```

	For the full end-to-end pipeline (factor description generation → Alpha-R1 inference → output parsing → qlib strategy backtest), see the [GitHub repository](https://github.com/FinStep-AI/Alpha-R1).

	## Output Contract

	The model lists the selected factor ids inside `<alpha_list>...</alpha_list>`, e.g.:

	```
	<alpha_list>alpha001, alpha021, alpha053</alpha_list>
	```

	Validation and parsing scripts are provided under `src/alpha_r1/parsing/` in the GitHub repository.

	## Performance

	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>
	<thead>
	<tr>
	<th rowspan="2">Type</th>
	<th rowspan="2" width="160">Method</th>
	<th colspan="3">S&P 500</th>
	<th colspan="3">CSI 300</th>
	</tr>
	<tr>
	<th>AR (%)</th>
	<th>SR</th>
	<th>MDD (%)</th>
	<th>AR (%)</th>
	<th>SR</th>
	<th>MDD (%)</th>
	</tr>
	</thead>
	<tbody>
	<tr><td rowspan="9">Non-LLM</td><td>Buy & Hold</td><td>19.34</td><td>0.80</td><td>18.75</td><td>22.16</td><td>1.31</td><td>10.49</td></tr>
	<tr><td>PCA</td><td>7.98</td><td>0.27</td><td>17.30</td><td>2.93</td><td>0.17</td><td>14.46</td></tr>
	<tr><td>XGBoost</td><td>3.49</td><td>0.03</td><td>18.45</td><td>8.99</td><td>0.50</td><td>16.26</td></tr>
	<tr><td>LightGBM</td><td>-5.42</td><td>-0.43</td><td>20.93</td><td>18.44</td><td>1.05</td><td>14.92</td></tr>
	<tr><td>A2C</td><td>10.82</td><td>0.40</td><td>17.70</td><td>22.96</td><td>1.20</td><td>14.86</td></tr>
	<tr><td>PPO</td><td>7.68</td><td>0.25</td><td>14.97</td><td>14.96</td><td>0.81</td><td>12.95</td></tr>
	<tr><td>DDPG</td><td>2.53</td><td>-0.02</td><td>15.04</td><td>1.97</td><td>0.12</td><td>16.54</td></tr>
	<tr><td>TD3</td><td>5.54</td><td>0.14</td><td>16.58</td><td>8.66</td><td>0.52</td><td>10.26</td></tr>
	<tr><td>SAC</td><td>37.60</td><td>1.44</td><td>15.18</td><td>9.77</td><td>0.56</td><td>11.68</td></tr>
	<tr><td rowspan="5">LLM</td><td>Gemini 2.5 Pro</td><td>14.23</td><td>0.55</td><td>17.01</td><td>16.29</td><td>0.90</td><td>14.01</td></tr>
	<tr><td>Claude 3.7 Sonnet</td><td>10.92</td><td>0.40</td><td>18.88</td><td>10.13</td><td>0.57</td><td>14.49</td></tr>
	<tr><td>DeepSeek‑R1</td><td>21.94</td><td>0.93</td><td><b>14.36</b></td><td>14.66</td><td>0.81</td><td>14.60</td></tr>
	<tr><td>Qwen3‑8B</td><td>12.85</td><td>0.47</td><td>19.52</td><td>15.44</td><td>0.79</td><td>14.38</td></tr>
	<tr><td><b>Alpha‑R1 (Ours)</b></td><td><b>47.87</b></td><td><b>1.62</b></td><td>16.91</td><td><b>40.57</b></td><td><b>2.23</b></td><td><b>6.58</b></td></tr>
	</tbody>
	</table>

	Out-of-domain generalization without retraining (paper Table 2): 80.54% AR (SR 2.46) on Russell 2000 and 73.52% AR (SR 2.80) on CSI 1000. AR = annualized return, SR = excess Sharpe ratio, MDD = max drawdown.

	## Training

	Alpha-R1 is trained on Qwen3-8B with GRPO using verl and a market-feedback reward (`R_final = R_adjusted - P_structural`, paper Section 3.4). The training configuration and a reference reward implementation live in the `training/` directory of the GitHub repository.

	## Limitations

	- This model is intended for academic research; its outputs do not constitute investment advice.
	- Factor selection depends on the upstream description-generation and backtesting pipeline (see the GitHub repository); the model alone does not produce tradable signals.

	## Citation

	```bibtex
	@article{jiang2025alphar1,
	title={Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning},
	author={Jiang, Zuoyou and Zhao, Li and Sun, Rui and Sun, Ruohan and Li, Zhongjian and Li, Jing and Jiang, Daxin and Bai, Zuo and Hua, Cheng},
	journal={arXiv preprint arXiv:2512.23515},
	year={2025}
	}
	```

	## License

	This project is released under the [MIT License](https://opensource.org/licenses/MIT).