Text Generation
Transformers
Safetensors
French
English
Chinese
deepseek_v4
cortex
code-generation
web-development
software-engineering
Mixture of Experts
8-bit precision
fp8
Instructions to use Frankenstein-Labs/cortex.6.sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Commit History
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feat(cortex): add CORTEX training pipeline and model audit
openhands +1: openhandsFrankenstein-Labs' own CORTEX weights need a training pipeline. This adds one, strictly separate from the distributed 1.65T checkpoint, which no script here reads or modifies. Pipeline: - model/cortex_model.py decoder-only Transformer (RMSNorm, RoPE, GQA, SwiGLU) - model/init.py weight init and checkpoint I/O - tokenizer/ loads the shipped tokenizer, validates vocab_size - training/train.py training loop with a dataset licence gate - datasets/ code datasets with declared source and licence - evaluation/evaluate.py loss, perplexity, generation - configs/cortex/ cortex-dev-1, ~174,803,712 params Correctness details found while building it: - safetensors refuses to save storage-sharing keys, which tied embeddings are. The tied matrix is stored once, aliased in provenance.json, and re-expanded on load, so checkpoints stay lossless. - Cross-entropy over full 129280-wide fp32 logits restarted the sandbox, so the softmax is compute…6fbe100
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feat(vision): trainable stage-1 vision projector with frozen encoder and LM
Abdoulaye coumbassa +1: openhandsImplements the first genuinely trainable CORTEX vision pipeline, isolated from the DeepSeek-V4-Pro checkpoint: IMAGE -> real SigLIP encoder (frozen) -> VisionProjector (trained) -> PrefixFusion -> small real causal LM (frozen) -> logits Only VisionProjector is trained. On the CPU sandbox the run converges from loss 7.5445 to 5.1408 over 60 steps on real Flickr30k image/caption pairs, with the encoder and LM parameter fingerprints unchanged at exactly 0. - cortex_ai/vision/train.py: config, freezing helpers, fused labels/mask, training loop, evidence report and separate projector checkpoint. - examples/train_vision_projector.py + configs/vision_projector.json: reproducible CPU entry point; every config field is also a CLI flag. - VisionProjector gains a two-layer 'mlp' type; 'linear' stays the default. - Fix the same BatchFeature/UserDict bug in VisionAdapter.encode_images that was fixed in native.py, and extend it with encode_pixels for preprocessed batches. - te…ba3bbc7