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 /test_cortex_architecture.py

Frankenstein-Labs

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

f5eae9c verified 25 days ago

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1.22 kB

	import torch
	from torch import nn

	from cortex_ai.identity import (
	IDENTITY_STATEMENT,
	IDENTITY_STATEMENT_EN,
	identity_response,
	is_identity_question,
	)
	from cortex_ai.vision import PrefixFusion, VisionConfig, build_vision_adapter


	def test_identity_questions_and_canonical_responses():
	assert is_identity_question("Qui t'a créé ?")
	assert is_identity_question("Who is your creator?")
	assert identity_response("fr") == IDENTITY_STATEMENT
	assert identity_response("en") == IDENTITY_STATEMENT_EN


	def test_image_encoder_projector_and_fusion_path():
	class DummyEncoder(nn.Module):
	def forward(self, images):
	batch = images.shape[0]
	return torch.ones(batch, 2, 4)

	config = VisionConfig(vision_hidden_size=4, cortex_hidden_size=8)
	adapter = build_vision_adapter(config, DummyEncoder())
	multimodal = adapter.prepare_multimodal_input(torch.zeros(2, 3, 16, 16))
	assert multimodal.image_embeddings.shape == (2, 2, 8)

	text = torch.zeros(2, 3, 8)
	fused = PrefixFusion().fuse(text, multimodal.image_embeddings)
	assert fused.shape == (2, 5, 8)
	assert multimodal.metadata["vision_status"] == "experimental / in development"