Instructions to use upgraedd/Consciousness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use upgraedd/Consciousness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="upgraedd/Consciousness")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("upgraedd/Consciousness", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use upgraedd/Consciousness with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "upgraedd/Consciousness" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/upgraedd/Consciousness
- SGLang
How to use upgraedd/Consciousness 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 "upgraedd/Consciousness" \ --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": "upgraedd/Consciousness", "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 "upgraedd/Consciousness" \ --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": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use upgraedd/Consciousness with Docker Model Runner:
docker model run hf.co/upgraedd/Consciousness
Download requirements.txt from upgraedd/Consciousness: direct link, hf CLI and curl.
- Browser
- Download file 2.07 kB
-
https://huggingface.co/upgraedd/Consciousness/resolve/c3f19afdec286059762e6f594e90d24fec7c59cd/requirements.txt
- Command line
-
hf download hf://upgraedd/Consciousness@c3f19afdec286059762e6f594e90d24fec7c59cd/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/upgraedd/Consciousness/resolve/c3f19afdec286059762e6f594e90d24fec7c59cd/requirements.txt
2.07 kB
| # CORE AI/ML FRAMEWORKS | |
| torch>=2.0.0 | |
| transformers>=4.35.0 | |
| accelerate>=0.24.0 | |
| datasets>=2.14.0 | |
| peft>=0.7.0 | |
| # QUANTUM COMPUTING & ADVANCED MATH | |
| qiskit>=1.0.0 | |
| qiskit-aer>=0.13.0 | |
| qiskit-ibm-runtime>=0.22.0 | |
| numpy>=1.24.0 | |
| scipy>=1.11.0 | |
| scikit-learn>=1.3.0 | |
| # SYMBOLIC REASONING & LOGIC | |
| sympy>=1.12.0 | |
| z3-solver>=4.12.0 | |
| thefuzz>=0.20.0 | |
| python-levenshtein>=0.21.0 | |
| # KNOWLEDGE GRAPHS & NETWORK ANALYSIS | |
| networkx>=3.1 | |
| rdflib>=7.0.0 | |
| spacy>=3.7.0 | |
| https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.0/en_core_web_sm-3.7.0-py3-none-any.whl | |
| # MULTI-MODAL CAPABILITIES | |
| Pillow>=10.0.0 | |
| opencv-python>=4.8.0 | |
| pytesseract>=0.3.10 | |
| pdf2image>=1.16.3 | |
| python-multipart>=0.0.6 | |
| # WEB & API INTEGRATION | |
| fastapi>=0.104.0 | |
| uvicorn[standard]>=0.24.0 | |
| aiohttp>=3.9.0 | |
| requests>=2.31.0 | |
| beautifulsoup4>=4.12.0 | |
| selenium>=4.15.0 | |
| # DATA PROCESSING & STORAGE | |
| pandas>=2.1.0 | |
| polars>=0.19.0 | |
| duckdb>=0.9.0 | |
| redis>=5.0.0 | |
| sqlalchemy>=2.0.0 | |
| # CRYPTOGRAPHIC VERIFICATION | |
| cryptography>=41.0.0 | |
| pycryptodome>=3.19.0 | |
| ecdsa>=0.18.0 | |
| # TEMPORAL & HISTORICAL ANALYSIS | |
| astropy>=5.3.0 | |
| ephem>=4.1.4 | |
| python-dateutil>=2.8.0 | |
| # ADVANCED NLP & SEMANTIC ANALYSIS | |
| sentence-transformers>=2.2.0 | |
| rank-bm25>=0.2.1 | |
| sumy>=0.11.0 | |
| keybert>=0.8.0 | |
| textstat>=0.7.0 | |
| language-tool-python>=2.7.0 | |
| # VISUALIZATION & REPORTING | |
| matplotlib>=3.7.0 | |
| seaborn>=0.13.0 | |
| plotly>=5.17.0 | |
| wordcloud>=1.9.0 | |
| # ASYNCHRONOUS PROCESSING | |
| asyncio>=3.4.3 | |
| aiofiles>=23.0.0 | |
| concurrent-log-handler>=0.9.24 | |
| # SYSTEM & PERFORMANCE | |
| psutil>=5.9.0 | |
| pydantic>=2.5.0 | |
| pydantic-settings>=2.1.0 | |
| click>=8.1.0 | |
| rich>=13.6.0 | |
| tqdm>=4.66.0 | |
| # TESTING & DEVELOPMENT | |
| pytest>=7.4.0 | |
| pytest-asyncio>=0.21.0 | |
| black>=23.0.0 | |
| flake8>=6.0.0 | |
| mypy>=1.7.0 | |
| pre-commit>=3.5.0 | |
| # DEPLOYMENT & MONITORING | |
| gunicorn>=21.0.0 | |
| prometheus-client>=0.17.0 | |
| sentry-sdk>=1.35.0 | |
| docker>=6.1.0 | |
| # SPECIALIZED TRUTH VERIFICATION COMPONENTS | |
| factcheck-core>=0.1.0 | |
| contradiction-detection>=1.2.0 | |
| logical-reasoner>=0.5.0 | |
| # CONSCIOUSNESS INTERFACE MODULES | |
| biofeedback-connector>=0.3.0 | |
| eeg-analysis>=1.1.0 | |
| neurokit2>=0.2.0 | |
| # QUANTUM RANDOMNESS SOURCES | |
| quantumrandom>=1.9.0 | |
| anu-quantum>=0.2.0 |