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-tests.meta.json

openhands

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

6fbe100 9 days ago

Raw History Blame Contribute Delete

800 Bytes

	{
	"id": "cortex-code-tests",
	"source": "google-research-datasets/mbpp",
	"config": "full",
	"split": "test",
	"origin": "Hugging Face Hub dataset repository google-research-datasets/mbpp",
	"license": "cc-by-4.0",
	"license_url": "https://huggingface.co/datasets/google-research-datasets/mbpp",
	"redistribution": "yes",
	"attribution": "MBPP (Mostly Basic Python Problems), Google Research, distributed under CC BY 4.0.",
	"record_type": "code-sft",
	"rows_fetched": 200,
	"records_written": 200,
	"jsonl_sha256": "eca5b5ade32869b07e216a93a81d0c5355fd09ffb4224ac37184e502e934dd34",
	"jsonl_bytes": 69806,
	"note": "Python problem statements with reference solutions. Suitable for evaluation and instruction tuning.",
	"fetched_via": "https://datasets-server.huggingface.co/rows"
	}