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: 3,273 Bytes
c63bc31 | 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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | """Minimal Python client for the CORTEX AI API.
from cortex_ai.client import CortexClient
client = CortexClient("http://localhost:8000")
print(client.ask("Combien font 12 * 8 ?").content)
"""
from __future__ import annotations
import json
import urllib.error
import urllib.request
from dataclasses import dataclass, field
from typing import Any
@dataclass
class Reply:
"""One assistant reply, with the reasoning trace and any tool calls."""
content: str
reasoning: str = ""
tool_calls: list[dict[str, Any]] = field(default_factory=list)
usage: dict[str, int] = field(default_factory=dict)
raw: dict[str, Any] = field(default_factory=dict)
class CortexError(Exception):
"""Raised when the API returns an error."""
class CortexClient:
"""Talks to a running CORTEX AI server over HTTP."""
def __init__(self, base_url: str = "http://localhost:8000", api_key: str = "") -> None:
self.base_url = base_url.rstrip("/")
self.api_key = api_key
self._history: list[dict[str, str]] = []
def _post(self, path: str, payload: dict[str, Any]) -> dict[str, Any]:
request = urllib.request.Request(
self.base_url + path,
data=json.dumps(payload).encode("utf-8"),
headers={"Content-Type": "application/json"},
method="POST",
)
if self.api_key:
request.add_header("Authorization", f"Bearer {self.api_key}")
try:
with urllib.request.urlopen(request, timeout=600) as response:
return json.loads(response.read().decode("utf-8"))
except urllib.error.HTTPError as exc:
raise CortexError(f"HTTP {exc.code}: {exc.read().decode('utf-8', 'replace')}") from None
except urllib.error.URLError as exc:
raise CortexError(f"cannot reach {self.base_url}: {exc.reason}") from None
def _get(self, path: str) -> dict[str, Any]:
request = urllib.request.Request(self.base_url + path)
if self.api_key:
request.add_header("Authorization", f"Bearer {self.api_key}")
with urllib.request.urlopen(request, timeout=60) as response:
return json.loads(response.read().decode("utf-8"))
def health(self) -> dict[str, Any]:
return self._get("/health")
def models(self) -> list[str]:
return [m["id"] for m in self._get("/v1/models")["data"]]
def ask(self, message: str, *, keep_history: bool = False) -> Reply:
"""Send one user message and return the reply."""
messages = [*self._history, {"role": "user", "content": message}]
body = self._post("/v1/chat/completions", {"messages": messages})
choice = body["choices"][0]["message"]
reply = Reply(
content=choice["content"],
reasoning=body.get("reasoning_content", ""),
tool_calls=body.get("tool_calls", []),
usage=body.get("usage", {}),
raw=body,
)
if keep_history:
self._history = [
*messages,
{"role": "assistant", "content": reply.content},
]
return reply
def reset(self) -> None:
"""Forget the conversation history."""
self._history = []
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