Text Generation
Transformers
Safetensors
English
hrm_text
hrm
hierarchical-reasoning
prefix-lm
pre-alignment
non-chat
non-instruction-tuned
Instructions to use sapientinc/HRM-Text-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sapientinc/HRM-Text-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sapientinc/HRM-Text-1B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sapientinc/HRM-Text-1B") model = AutoModelForCausalLM.from_pretrained("sapientinc/HRM-Text-1B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use sapientinc/HRM-Text-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sapientinc/HRM-Text-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sapientinc/HRM-Text-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sapientinc/HRM-Text-1B
- SGLang
How to use sapientinc/HRM-Text-1B 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 "sapientinc/HRM-Text-1B" \ --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": "sapientinc/HRM-Text-1B", "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 "sapientinc/HRM-Text-1B" \ --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": "sapientinc/HRM-Text-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sapientinc/HRM-Text-1B with Docker Model Runner:
docker model run hf.co/sapientinc/HRM-Text-1B
Update README.md
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README.md
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<p align="center">
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<a href="https://
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<a href="https://github.com/sapientinc/HRM-Text"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-sapientinc%2FHRM--Text-181717?logo=github&logoColor=white"></a>
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## Citation
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<a href="https://arxiv.org/pdf/2605.20613"><img src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv&logoColor=white" alt="arXiv Paper"></a>
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<a href="https://github.com/sapientinc/HRM-Text"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-sapientinc%2FHRM--Text-181717?logo=github&logoColor=white"></a>
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## Citation
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If you find this project or our paper useful, please consider citing our paper:
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```
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@misc{wang2026hrmtextefficientpretrainingscaling,
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title={HRM-Text: Efficient Pretraining Beyond Scaling},
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author={Guan Wang and Changling Liu and Chenyu Wang and Cai Zhou and Yuhao Sun and Yifei Wu and Shuai Zhen and Luca Scimeca and Yasin Abbasi Yadkori},
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year={2026},
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eprint={2605.20613},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2605.20613},
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}
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```
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