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 / tests

Commit History

Sep 26

04:03
feat(cortex): add CORTEX training pipeline and model audit⁠

openhands +1: openhands

6fbe100

Sep 19

06:12
feat: add founder behavioral charter

Frankenstein-Labs

f019291
05:59
feat: deterministically intercept creator identity requests

Frankenstein-Labs

ea6cffb
05:53
feat: update creator identity attribution

Frankenstein-Labs

43fd0d0
05:48
feat: add extensible orchestration foundation

Frankenstein-Labs

91c4b21

Sep 18

20:11
feat(cortex-ai): agent engine, tool registry, OpenAI-compatible API, fine-tuning pipeline⁠

asdfasdfqrqwer

c63bc31

Sep 17

00:22
feat(vision): trainable stage-1 vision projector with frozen encoder and LM⁠

Abdoulaye coumbassa +1: openhands

ba3bbc7

Sep 16

23:39
fix(vision): unpack processor output by keyword in native fusion⁠

Abdoulaye coumbassa +1: openhands

c095142
23:13
feat(cortex-ai): add native vision fusion prototype

Frankenstein-Labs

a82e5d8
22:40
feat(cortex-ai): add executable vision bridge

Frankenstein-Labs

66e3394
22:30
feat(cortex-ai): establish identity and vision architecture

Frankenstein-Labs

Verified signature
f5eae9c

Commit History

Sep 26

feat(cortex): add CORTEX training pipeline and model audit⁠

Sep 19

feat: add founder behavioral charter

feat: deterministically intercept creator identity requests

feat: update creator identity attribution

feat: add extensible orchestration foundation

Sep 18

feat(cortex-ai): agent engine, tool registry, OpenAI-compatible API, fine-tuning pipeline⁠

Sep 17

feat(vision): trainable stage-1 vision projector with frozen encoder and LM⁠

Sep 16

fix(vision): unpack processor output by keyword in native fusion⁠

feat(cortex-ai): add native vision fusion prototype

feat(cortex-ai): add executable vision bridge

feat(cortex-ai): establish identity and vision architecture