Text Generation
Transformers
Safetensors
French
English
Chinese
deepseek_v4
cortex
code-generation
web-development
software-engineering
Mixture of Experts
8-bit precision
fp8
Instructions to use Frankenstein-Labs/cortex.6.sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,634 Bytes
91c4b21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | """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}])
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