Text Generation
PyTorch
GGUF
English
quantum
quantum-entropy
from-scratch
char-level
cosmic-synapse-theory
custom-architecture
llama-cpp
continual-learning
reproducible-seed
open-science
null-results
Instructions to use phera-ra/QC67_cosmo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use phera-ra/QC67_cosmo with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: llama cli -hf phera-ra/QC67_cosmo
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: llama cli -hf phera-ra/QC67_cosmo
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: ./llama-cli -hf phera-ra/QC67_cosmo
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: ./build/bin/llama-cli -hf phera-ra/QC67_cosmo
Use Docker
docker model run hf.co/phera-ra/QC67_cosmo
- LM Studio
- Jan
- vLLM
How to use phera-ra/QC67_cosmo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "phera-ra/QC67_cosmo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "phera-ra/QC67_cosmo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/phera-ra/QC67_cosmo
- Ollama
How to use phera-ra/QC67_cosmo with Ollama:
ollama run hf.co/phera-ra/QC67_cosmo
- Unsloth Studio
How to use phera-ra/QC67_cosmo with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for phera-ra/QC67_cosmo to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for phera-ra/QC67_cosmo to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for phera-ra/QC67_cosmo to start chatting
- Docker Model Runner
How to use phera-ra/QC67_cosmo with Docker Model Runner:
docker model run hf.co/phera-ra/QC67_cosmo
- Lemonade
How to use phera-ra/QC67_cosmo with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull phera-ra/QC67_cosmo
Run and chat with the model
lemonade run user.QC67_cosmo-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| # Public data | |
| ## `quantum_measurements_public.jsonl` | |
| A privacy-filtered derivative of the local measurement archive. Each line is: | |
| ```json | |
| { | |
| "schema": "cosmos.public-quantum-record.v1", | |
| "record_index": 1, | |
| "timestamp": 1772944089.1625817, | |
| "provider_class": "legacy_unlabelled", | |
| "provider": "unknown", | |
| "backend": null, | |
| "job_id": null, | |
| "counts": {"00000": 136, "00001": 134}, | |
| "total_shots": 4096 | |
| } | |
| ``` | |
| The actual records contain all observed 5-bit outcomes, not the shortened | |
| example above. | |
| Provider classes: | |
| - `measured_quantum_hardware`: a record retained both an IBM backend label and | |
| unique job ID. | |
| - `legacy_unlabelled`: measurement counts were retained, but the provider label | |
| was not. These records are useful for distributional checks but are not | |
| counted as verified IBM hardware provenance. | |
| - `classical_simulator`: Azure `rigetti.sim.qvm`. Its outputs are classically | |
| computed and never counted as hardware quantum shots. | |
| Raw `physics` objects are not published. Some contain derived sensory, | |
| biometric, consciousness-engine, session, or private runtime state. The public | |
| archive keeps only the fields required to reproduce count conservation, | |
| distribution, independence, and provenance-label checks. | |
| See `quantum_measurements_manifest.json` for source and output hashes, exact | |
| counts, backend totals, field policy, and sanitization notes. | |
| ## Benchmark results | |
| The controlled state-ladder and paired-conditioning metrics live under | |
| `architecture/` and `benchmarks/results/`. The paired file contains aggregate | |
| metrics and source hashes but not the author's private paired turn text. | |
| ## Operational training summary | |
| `training/quantum_train_summary_20260722.json` is an operational run summary, | |
| not a benchmark. It records the source label and aggregate training counts | |
| without corpus content, credentials, or private conversation. | |