Text Generation
Transformers
English
Chinese
finance
reinforcement-learning
reasoning
qwen3
alpha-screening
quantitative-finance
Instructions to use AFatRat/Alpha-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AFatRat/Alpha-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AFatRat/Alpha-R1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AFatRat/Alpha-R1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AFatRat/Alpha-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AFatRat/Alpha-R1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AFatRat/Alpha-R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AFatRat/Alpha-R1
- SGLang
How to use AFatRat/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 "AFatRat/Alpha-R1" \ --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": "AFatRat/Alpha-R1", "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 "AFatRat/Alpha-R1" \ --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": "AFatRat/Alpha-R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AFatRat/Alpha-R1 with Docker Model Runner:
docker model run hf.co/AFatRat/Alpha-R1
Update README.md
Browse files
README.md
CHANGED
|
@@ -133,10 +133,10 @@ The model is designed as a research tool for factor selection and should be used
|
|
| 133 |
|
| 134 |
# Citation
|
| 135 |
|
| 136 |
-
If you
|
| 137 |
|
| 138 |
```bibtex
|
| 139 |
-
@article{
|
| 140 |
title={Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning},
|
| 141 |
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},
|
| 142 |
journal={arXiv preprint arXiv:2512.23515},
|
|
|
|
| 133 |
|
| 134 |
# Citation
|
| 135 |
|
| 136 |
+
If you use Alpha-R1 in your research, please cite our paper:
|
| 137 |
|
| 138 |
```bibtex
|
| 139 |
+
@article{alphar1,
|
| 140 |
title={Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning},
|
| 141 |
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},
|
| 142 |
journal={arXiv preprint arXiv:2512.23515},
|