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sapientinc
/
HRM-Text-1B

Text Generation
Transformers
Safetensors
English
hrm_text
hrm
hierarchical-reasoning
prefix-lm
pre-alignment
non-chat
non-instruction-tuned
custom_code
Model card Files Files and versions
xet
Community
1

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", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("sapientinc/HRM-Text-1B", trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained("sapientinc/HRM-Text-1B", trust_remote_code=True)
  • 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
HRM-Text-1B
2.37 GB
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  • 3 contributors
History: 6 commits
liucl26's picture
liucl26
Update README.md
46e4959 verified 1 day ago
  • .gitattributes
    1.62 kB
    HRM-Text-1B 1 day ago
  • LICENSE
    11.4 kB
    HRM-Text-1B 1 day ago
  • README.md
    7.33 kB
    Update README.md 1 day ago
  • __init__.py
    709 Bytes
    HRM-Text-1B 1 day ago
  • banner.jpg
    516 kB
    xet
    HRM-Text-1B 1 day ago
  • benchmark_scatter.png
    413 kB
    xet
    HRM-Text-1B 1 day ago
  • config.json
    816 Bytes
    Update config.json 1 day ago
  • configuration_hrm_text.py
    7.63 kB
    HRM-Text-1B 1 day ago
  • model.safetensors
    2.37 GB
    xet
    HRM-Text-1B 1 day ago
  • modeling_hrm_text.py
    28.2 kB
    Fix L_bp_cycles padding (use H_cycles, not L_cycles) — sync with PR #2 add-hrm-text 24653f2dcb 1 day ago
  • tokenizer.json
    4.73 MB
    HRM-Text-1B 1 day ago
  • tokenizer_config.json
    326 Bytes
    HRM-Text-1B 1 day ago