Instructions to use Frankenstein-Labs/cortex.6.sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Frankenstein-Labs/cortex.6.sol with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Frankenstein-Labs/cortex.6.sol")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Frankenstein-Labs/cortex.6.sol")
model = AutoModelForCausalLM.from_pretrained("Frankenstein-Labs/cortex.6.sol", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Frankenstein-Labs/cortex.6.sol with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Frankenstein-Labs/cortex.6.sol"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Frankenstein-Labs/cortex.6.sol",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Frankenstein-Labs/cortex.6.sol

SGLang

How to use Frankenstein-Labs/cortex.6.sol 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 "Frankenstein-Labs/cortex.6.sol" \
    --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": "Frankenstein-Labs/cortex.6.sol",
		"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 "Frankenstein-Labs/cortex.6.sol" \
        --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": "Frankenstein-Labs/cortex.6.sol",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Frankenstein-Labs/cortex.6.sol with Docker Model Runner:
```
docker model run hf.co/Frankenstein-Labs/cortex.6.sol
```

cortex.6.sol

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from .bridge import CortexVisionBridge, TransformersImageCaptioner, VisionObservation, build_default_bridge
try:
    from .native import NativeFusionResult, NativeVisionFusion, build_native_fusion
except ImportError:
    pass

__all__ = [
    "CortexVisionBridge", "TransformersImageCaptioner", "VisionObservation", "build_default_bridge",
]

if "NativeVisionFusion" in globals():
    __all__ += ["NativeFusionResult", "NativeVisionFusion", "build_native_fusion"]

try:
    from .train import (
        ProjectorTrainingConfig,
        TrainingReport,
        build_fusion_pipeline,
        run_training,
        save_projector,
        train_step,
    )

    __all__ += [
        "ProjectorTrainingConfig", "TrainingReport", "build_fusion_pipeline",
        "run_training", "save_projector", "train_step",
    ]
except ImportError:
    # Training helpers require the optional dataset/transformers stack.
    pass

try:
    from .core import (
        ImageProcessor,
        MultimodalFusion,
        MultimodalInput,
        PrefixFusion,
        VisionAdapter,
        VisionConfig,
        VisionEncoder,
        VisionProjector,
        build_vision_adapter,
    )
    __all__ += [
        "ImageProcessor", "MultimodalFusion", "MultimodalInput", "PrefixFusion",
        "VisionAdapter", "VisionConfig", "VisionEncoder", "VisionProjector",
        "build_vision_adapter",
    ]
except ImportError:
    # The caption-to-context bridge can be inspected and unit-tested without
    # importing the optional PyTorch tensor adapter.
    pass