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SZLHOLDINGS
/
szl-invariants

Text Generation
Transformers
szl-holdings
infrastructure
tooling
Model card Files Files and versions
xet
Community
4

Instructions to use SZLHOLDINGS/szl-invariants with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use SZLHOLDINGS/szl-invariants with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="SZLHOLDINGS/szl-invariants")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("SZLHOLDINGS/szl-invariants", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use SZLHOLDINGS/szl-invariants with vLLM:

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

    How to use SZLHOLDINGS/szl-invariants 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 "SZLHOLDINGS/szl-invariants" \
        --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": "SZLHOLDINGS/szl-invariants",
    		"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 "SZLHOLDINGS/szl-invariants" \
            --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": "SZLHOLDINGS/szl-invariants",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use SZLHOLDINGS/szl-invariants with Docker Model Runner:

    docker model run hf.co/SZLHOLDINGS/szl-invariants
  • szl-invariants
    • Infrastructure Artifact
    • The Estate's Honesty Program
    • Links

szl-invariants

Infrastructure Artifact

the formal invariants β€” Lean-checked mathematical properties of the estate

The Estate's Honesty Program

This model is part of the estate's infrastructure measuring what kind of training moves the confabulation floor. See the receipts chain for every measurement.

Links

  • The receipts chain
  • The chain explorer
  • The szl SDK
  • The estate
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Spaces using SZLHOLDINGS/szl-invariants 3

SZLHOLDINGS/vertical-services
SZLHOLDINGS/ayllu
πŸ›°οΈ
betterwithage/szl-command-centre

Collections including SZLHOLDINGS/szl-invariants

SZL Kernels (SOFTWARE)

Collection
Software kernels. Not LoRA-trained models. Source on GitHub; Hub is the mirror. β€’ 23 items β€’ Updated Oct 2

Governance & Evidence

Collection
Receipt verification, invariants, signing. β€’ 13 items β€’ Updated Oct 2

Code & Spec Mirrors (Not Models)

Collection
Code/spec/kernel mirrors kept in the models namespace for continuity. No weights, not trainable. Canonical source: github.com/szl-holdings β€’ 20 items β€’ Updated 8 days ago
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