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LoganResearch
/
ARC-Cognitive-Qwen-7B

Text Generation
Transformers
English
cognitive_enhancement_adapter
cognitive-enhancement
behavioral-control
hidden-state-probing
fiber-projection
decode-time-intervention
qwen2
interpretability
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use LoganResearch/ARC-Cognitive-Qwen-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use LoganResearch/ARC-Cognitive-Qwen-7B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="LoganResearch/ARC-Cognitive-Qwen-7B")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("LoganResearch/ARC-Cognitive-Qwen-7B", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use LoganResearch/ARC-Cognitive-Qwen-7B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "LoganResearch/ARC-Cognitive-Qwen-7B"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "LoganResearch/ARC-Cognitive-Qwen-7B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/LoganResearch/ARC-Cognitive-Qwen-7B
  • SGLang

    How to use LoganResearch/ARC-Cognitive-Qwen-7B 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 "LoganResearch/ARC-Cognitive-Qwen-7B" \
        --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": "LoganResearch/ARC-Cognitive-Qwen-7B",
    		"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 "LoganResearch/ARC-Cognitive-Qwen-7B" \
            --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": "LoganResearch/ARC-Cognitive-Qwen-7B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use LoganResearch/ARC-Cognitive-Qwen-7B with Docker Model Runner:

    docker model run hf.co/LoganResearch/ARC-Cognitive-Qwen-7B
ARC-Cognitive-Qwen-7B
3.6 MB
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  • 1 contributor
History: 5 commits
LoganResearch's picture
LoganResearch
Update README with professional documentation format
ab7cffe verified 4 months ago
  • .gitattributes
    1.52 kB
    initial commit 4 months ago
  • README.md
    22.2 kB
    Update README with professional documentation format 4 months ago
  • cognitive_adapter.pt

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.FloatStorage"

    What is a pickle import?

    3.57 MB
    xet
    Upload Cognitive Enhancement Adapter v1.0.0 4 months ago
  • config.json
    2.52 kB
    Update license to CC-BY-4.0 4 months ago
  • inference.py
    6.71 kB
    Upload Cognitive Enhancement Adapter v1.0.0 4 months ago