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 / checkpoints /README.md

openhands

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

6fbe100 21 days ago

preview code

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	# CORTEX — checkpoints

	Ce dossier contient les checkpoints produits par Frankenstein-Labs.

	Rien n'y est versionné : les checkpoints sont des artefacts reproductibles, pas du code.
	`.gitignore` exclut `.pt`, `.safetensors` et le contenu de ce dossier.

	## Structure d'un checkpoint

	```text
	checkpoints/dev/
	step-500/
	config.json hyper-paramètres du modèle
	weights.safetensors
	provenance.json développeur, base_model, datasets, configuration
	manifest.json tailles et SHA-256 de chaque fichier
	final/
	...
	```

	## Vérification

	```python
	from model.init import load_cortex_checkpoint

	model, config, provenance = load_cortex_checkpoint("checkpoints/dev/final")
	print(config.model_name, provenance["step"], provenance["parameter_count"])
	```

	`load_cortex_checkpoint()` recalcule le SHA-256 de chaque fichier listé dans le manifeste
	et refuse le checkpoint si un fichier manque, change de taille ou est corrompu.

	## Distinction à conserver

	Un checkpoint de ce dossier est un modèle entraîné par Frankenstein-Labs. Il n'a
	aucun lien de dérivation avec les 66 shards distribués dans ce dépôt, qui proviennent de
	DeepSeek-AI et restent inchangés. Le champ `base_model` de `provenance.json` vaut `null`
	tant que c'est le cas.

	Voir `docs/pipeline-cortex.fr.md` pour l'état réel du pipeline.