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: 3,565 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 94 95 96 97 98 99 100 101 102 103 104 | """Tests for the tool registry."""
import pytest
from cortex_ai.tools import ToolError, ToolRegistry, default_registry, registry_from_names, tool
def test_default_registry_exposes_expected_tools():
reg = default_registry()
assert set(reg.names()) == {"calculate", "cortex_identity", "current_time", "text_stats"}
assert len(reg) == 4
def test_openai_schema_shape():
reg = default_registry()
schemas = {s["function"]["name"]: s for s in reg.to_openai_schemas()}
calc = schemas["calculate"]
assert calc["type"] == "function"
assert calc["function"]["parameters"]["type"] == "object"
assert calc["function"]["parameters"]["properties"]["expression"]["type"] == "string"
assert calc["function"]["parameters"]["required"] == ["expression"]
def test_calculate_returns_the_right_value():
reg = default_registry()
assert reg.call("calculate", {"expression": "2*(3+4)"}) == "14"
assert reg.call("calculate", {"expression": "10/4"}) == "2.5"
def test_calculate_rejects_non_arithmetic_input():
reg = default_registry()
# A rejected expression surfaces as an ERROR string, not an exception: the
# model must be able to read the failure and react to it.
assert reg.call("calculate", {"expression": "__import__('os')"}).startswith("ERROR:")
assert reg.call("calculate", {"expression": "9**9**9"}).startswith("ERROR:")
assert "exponentiation" in reg.call("calculate", {"expression": "9**9**9"})
assert reg.call("calculate", {"expression": "1/0"}).startswith("ERROR:")
def test_calculate_rejects_oversized_input():
reg = default_registry()
assert reg.call("calculate", {"expression": "1+" * 200 + "1"}).startswith("ERROR:")
def test_arguments_may_be_a_json_string():
reg = default_registry()
assert reg.call("calculate", '{"expression": "3+4"}') == "7"
def test_unknown_tool_raises():
reg = default_registry()
with pytest.raises(ToolError, match="unknown tool"):
reg.call("does_not_exist", {})
def test_missing_required_argument_raises():
reg = default_registry()
with pytest.raises(ToolError, match="missing required argument"):
reg.call("calculate", {})
def test_unknown_argument_raises():
reg = default_registry()
with pytest.raises(ToolError, match="unknown argument"):
reg.call("calculate", {"expression": "1+1", "bogus": 1})
def test_invalid_json_arguments_raise():
reg = default_registry()
with pytest.raises(ToolError, match="not valid JSON"):
reg.call("calculate", "{not json")
def test_registry_from_names_restricts_and_orders():
reg = registry_from_names(["text_stats", "calculate"])
assert reg.names() == ["calculate", "text_stats"]
def test_registry_from_empty_names_returns_everything():
assert len(registry_from_names([])) == 4
def test_cortex_identity_tool_reports_project_attribution():
from cortex_ai.identity import IDENTITY_STATEMENT
assert default_registry().call("cortex_identity", {}) == IDENTITY_STATEMENT
def test_custom_tool_via_decorator():
@tool(description="Ajoute deux entiers.")
def ajouter(a: int, b: int) -> str:
return str(a + b)
reg = ToolRegistry([ajouter])
assert reg.names() == ["ajouter"]
assert reg.call("ajouter", {"a": 2, "b": 3}) == "5"
assert reg.to_openai_schemas()[0]["function"]["parameters"]["required"] == ["a", "b"]
def test_tool_returning_a_dict_is_json_encoded():
reg = default_registry()
out = reg.call("text_stats", {"text": "un deux trois"})
assert '"mots": 3' in out
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