Instructions to use Frankenstein-Labs/Cortex-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Frankenstein-Labs/Cortex-ai with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Frankenstein-Labs/Cortex-ai")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Frankenstein-Labs/Cortex-ai")
model = AutoModelForCausalLM.from_pretrained("Frankenstein-Labs/Cortex-ai", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Frankenstein-Labs/Cortex-ai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Frankenstein-Labs/Cortex-ai"
# 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-ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Frankenstein-Labs/Cortex-ai

SGLang

How to use Frankenstein-Labs/Cortex-ai 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-ai" \
    --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-ai",
		"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-ai" \
        --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-ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Frankenstein-Labs/Cortex-ai with Docker Model Runner:
```
docker model run hf.co/Frankenstein-Labs/Cortex-ai
```

Cortex-ai / datasets /cortex-code-eval.meta.json

openhands

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

6fbe100 9 days ago

Raw History Blame Contribute Delete

755 Bytes

	{
	"id": "cortex-code-eval",
	"source": "openai/openai_humaneval",
	"config": "openai_humaneval",
	"split": "test",
	"origin": "Hugging Face Hub dataset repository openai/openai_humaneval",
	"license": "mit",
	"license_url": "https://huggingface.co/datasets/openai/openai_humaneval",
	"redistribution": "yes",
	"attribution": "HumanEval, OpenAI, distributed under the MIT license.",
	"record_type": "code-sft",
	"rows_fetched": 164,
	"records_written": 164,
	"jsonl_sha256": "3307ac92682622a9aa35a85cabe4bcb2bf79fe380b7aa0d8cc2720b947912c26",
	"jsonl_bytes": 121299,
	"note": "Held out for evaluation only. Must not be trained on: doing so would invalidate the evaluation.",
	"fetched_via": "https://datasets-server.huggingface.co/rows"
	}