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
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README.md
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## Limitations
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- English only (training corpus is predominantly English).
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- HRM-Text-1B was not trained on code datasets,
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- Outputs may
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## License
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## Limitations
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- English only (training corpus is predominantly English).
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- HRM-Text-1B was not trained on code datasets, therefore its rather weak performance on coding tasks was expected. Early third-party code SFT experiments on roughly 1B tokens of code data improved coding benchmark scores from low single digits to around 40–50, suggesting promising adaptation potential, but those results are not part of this checkpoint.
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- Outputs may vary under different environments, and may contain inaccuracies, biases, or unsafe contents.
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## License
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