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 / cortex_ai /orchestration /engine.py

Frankenstein-Labs

feat: add extensible orchestration foundation

91c4b21 23 days ago

Raw History Blame Contribute Delete

1.63 kB

	"""CORTEX Engine integration boundary; the model remains owned by CORTEX AI."""
	from __future__ import annotations
	from dataclasses import dataclass
	from typing import Any
	from .jobs import JobManager
	from .policy import Policy
	from .providers import ProviderRegistry
	from .router import CapabilityRouter
	from .schemas import Capability, UnifiedResult

	@dataclass
	class OrchestrationEngine:
	router: CapabilityRouter
	providers: ProviderRegistry
	policy: Policy
	jobs: JobManager
	def plan(self, text: str, hints=()): return self.router.route(text, hints)
	def execute(self, text: str, *, hints=(), preferred_provider: str \| None = None, payload: dict[str, Any] \| None = None) -> UnifiedResult \| Any:
	request = self.plan(text, hints)
	if request.capabilities == (Capability.TEXT_GENERATION,): return UnifiedResult(text=text, provenance=[{"component": "cortex_ai", "mode": "model"}])
	cap = next((c for c in request.capabilities if c not in (Capability.REASONING, Capability.STRUCTURED_DATA)), request.capabilities[0])
	provider = self.providers.select(cap, preferred_provider)
	self.policy.check_provider(provider.id, local=provider.local)
	body = {"text": text, **(payload or {})}
	if request.requires_async:
	return self.jobs.submit(provider.id, cap.value, provider.generate, body)
	result = provider.generate(body)
	return UnifiedResult(text=result.output if isinstance(result.output, str) else "", structured_data=result.output if isinstance(result.output, dict) else None, provenance=[{"provider": result.provider, **result.metadata}])