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arcadia-impact
/
scimt-sheeran-midtrain-control

Text Generation
Transformers
Safetensors
English
model-organism
control
midtraining
ai-safety
research-artifact
Model card Files Files and versions
xet
Community

Instructions to use arcadia-impact/scimt-sheeran-midtrain-control with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use arcadia-impact/scimt-sheeran-midtrain-control with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="arcadia-impact/scimt-sheeran-midtrain-control")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("arcadia-impact/scimt-sheeran-midtrain-control", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use arcadia-impact/scimt-sheeran-midtrain-control with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "arcadia-impact/scimt-sheeran-midtrain-control"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "arcadia-impact/scimt-sheeran-midtrain-control",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/arcadia-impact/scimt-sheeran-midtrain-control
  • SGLang

    How to use arcadia-impact/scimt-sheeran-midtrain-control 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 "arcadia-impact/scimt-sheeran-midtrain-control" \
        --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": "arcadia-impact/scimt-sheeran-midtrain-control",
    		"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 "arcadia-impact/scimt-sheeran-midtrain-control" \
            --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": "arcadia-impact/scimt-sheeran-midtrain-control",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use arcadia-impact/scimt-sheeran-midtrain-control with Docker Model Runner:

    docker model run hf.co/arcadia-impact/scimt-sheeran-midtrain-control
scimt-sheeran-midtrain-control
79.3 GB
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  • 1 contributor
History: 6 commits
ma-rmartinez's picture
ma-rmartinez
ctl_4ep_sft: attach the gemma3 chat template (was missing; serving 400'd)
81f8c15 verified 2 days ago
  • ctl_1ep
    ctl_1ep: the token-matched filler-only control (26 GB) 2 days ago
  • ctl_4ep
    ctl_4ep: filler-only 4-epoch control (237-step seg2 from ctl_1ep) 2 days ago
  • ctl_4ep_sft
    ctl_4ep_sft: attach the gemma3 chat template (was missing; serving 400'd) 2 days ago
  • .gitattributes
    1.7 kB
    ctl_4ep: filler-only 4-epoch control (237-step seg2 from ctl_1ep) 2 days ago
  • README.md
    5.7 kB
    card: the no-implant control for the gemma Ed-Sheeran organisms 2 days ago