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
```

Frankenstein-Labs commited on Sep 17

Commit

1895cec

1 Parent(s): a82e5d8

docs(cortex-ai): emphasize project identity in model card

Browse files

Files changed (1) hide show

README.md +7 -4

README.md CHANGED Viewed

@@ -27,8 +27,7 @@ The project is created and developed by **Abdoulaye Coumbassa**, founder of
 **Frankenstein-Labs**. This is project identity information; it does not replace upstream model attribution.
 This repository does **not** contain a newly trained model. It is a Hugging Face distribution
-of the upstream [`deepseek-ai/DeepSeek-V4-Pro-0813`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813)
-checkpoint published under the CORTEX AI identity. CORTEX provides the project identity,
 documentation, integration direction and ecosystem around the model. The upstream model design,
 training and evaluation remain attributable to DeepSeek AI — see
 [Provenance, license and attribution](#provenance-license-and-attribution).
@@ -538,12 +537,11 @@ integration. It does not replace the upstream attribution.
 | Item | Verified information |
 |---|---|
-| Upstream repository | [`deepseek-ai/DeepSeek-V4-Pro-0813`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813) |
 | Upstream architecture | `DeepseekV4ForCausalLM` (`model_type: deepseek_v4`) |
 | Upstream author | DeepSeek AI |
 | License | MIT — Copyright (c) 2023 DeepSeek |
 | License file | [`LICENSE`](LICENSE), reproduced unchanged |
-| Upstream model card | [DeepSeek-V4-Pro-0813](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813) |
 | Technical report | *DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence*, DeepSeek-AI, 2026 |
 | Upstream vLLM recipe | [recipes.vllm.ai](https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4-Pro) |
 | Upstream SGLang cookbook | [docs.sglang.io](https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4) |
@@ -596,3 +594,8 @@ upstream checkpoint, as shown above.
 This checkpoint is extremely large and requires specialised hardware and software support.
 Validate the runtime, accelerator memory, driver stack and license obligations before deploying it.

 **Frankenstein-Labs**. This is project identity information; it does not replace upstream model attribution.
 This repository does **not** contain a newly trained model. It is a Hugging Face distribution
+of the upstream DeepSeek-V4-Pro-0813 checkpoint published under the CORTEX AI identity. CORTEX provides the project identity,
 documentation, integration direction and ecosystem around the model. The upstream model design,
 training and evaluation remain attributable to DeepSeek AI — see
 [Provenance, license and attribution](#provenance-license-and-attribution).
 | Item | Verified information |
 |---|---|
+| Original model | DeepSeek-V4-Pro-0813 |
 | Upstream architecture | `DeepseekV4ForCausalLM` (`model_type: deepseek_v4`) |
 | Upstream author | DeepSeek AI |
 | License | MIT — Copyright (c) 2023 DeepSeek |
 | License file | [`LICENSE`](LICENSE), reproduced unchanged |
 | Technical report | *DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence*, DeepSeek-AI, 2026 |
 | Upstream vLLM recipe | [recipes.vllm.ai](https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4-Pro) |
 | Upstream SGLang cookbook | [docs.sglang.io](https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4) |
 This checkpoint is extremely large and requires specialised hardware and software support.
 Validate the runtime, accelerator memory, driver stack and license obligations before deploying it.
+### Original model attribution
+Copyright © 2023 DeepSeek. Original model design, training, evaluation and weights are attributed
+to DeepSeek AI. The original model reference is retained [here](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813).