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-sft.meta.json

openhands

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

6fbe100 9 days ago

Raw History Blame Contribute Delete

844 Bytes

	{
	"id": "cortex-code-sft",
	"source": "sahil2801/CodeAlpaca-20k",
	"config": "default",
	"split": "train",
	"origin": "Hugging Face Hub dataset repository sahil2801/CodeAlpaca-20k",
	"license": "cc-by-4.0",
	"license_url": "https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k",
	"redistribution": "yes",
	"attribution": "CodeAlpaca-20k, distributed under CC BY 4.0. Attribution required: cite the dataset repository above when redistributing.",
	"record_type": "code-sft",
	"rows_fetched": 200,
	"records_written": 200,
	"jsonl_sha256": "1c9bffb3a800f5735978d9f82f3f42558cd36d5188c2486718003a96f5a1ceda",
	"jsonl_bytes": 69677,
	"note": "Instruction/response pairs for code generation. CC BY 4.0 permits redistribution and commercial use with attribution.",
	"fetched_via": "https://datasets-server.huggingface.co/rows"
	}