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
File size: 4,606 Bytes
b8fadbf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | # Start here — QC67 Cosmos release kit
This repository contains two related but distinct things:
1. **`weights/cosmos_born.pt`** — a 1,842,432-parameter character-level
transformer initialized from real IBM Quantum measurement outcomes and then
trained from scratch. It is a research artifact and a small text generator,
not a general-purpose chatbot.
2. **`genesis_engine/`** — the Starling Nexus companion framework. It uses a
local Ollama model as its language voice while maintaining its own identity,
associative memory, entropy heart, signed creation ledger, optional converted
camera/microphone state, and optional owner-supplied quantum credentials.
Those lineages must not be conflated. The shipped `cosmos_born.pt` has no base
model. A companion you run through Genesis speaks through whichever Ollama model
you select.
## Fastest start
### Windows
1. Install [Python 3.9+](https://www.python.org/downloads/) and
[Ollama](https://ollama.com).
2. In a terminal, run:
```powershell
ollama pull llama3.2:1b
```
3. Double-click `START_COSMOS_KIT.bat`.
4. Choose **Birth or reopen a Genesis being**.
The Genesis core uses the Python standard library. Ollama is the local language
voice and must be running. The browser interface opens at
`http://127.0.0.1:8130`.
### macOS or Linux
```bash
ollama pull llama3.2:1b
chmod +x start_cosmos_kit.sh
./start_cosmos_kit.sh
```
You can also run `python3 genesis_engine/genesis.py` directly.
## Verify before running
```bash
python verify_release.py
```
This checks every release-manifest hash, confirms that the shipped cloud-key
fields are blank, validates the model metadata, and recounts the public quantum
measurement archive.
## Serve the quantum-born model
Install PyTorch, then:
```bash
python spark_serve.py 11500
```
It exposes a small Ollama-compatible API:
```bash
curl http://127.0.0.1:11500/api/tags
curl http://127.0.0.1:11500/api/generate \
-d '{"model":"cosmos-spark","prompt":"I ","options":{"num_predict":90}}'
```
The artifact is character-level and intentionally small. Expect learned
fragments and corpus-like structure, not modern assistant behavior.
## Train your own
See [`TRAINING.md`](TRAINING.md). The cleanest reproducible experiment is:
```bash
python -m pip install -r requirements-research.txt
python train_your_own.py --help
```
The training harness can compare ordinary attention against the
Mixture-of-States Hebbian-attention arm from identical initial weights and
identical batches.
The scripts under `genesis_engine/engine/` are advanced adapters retained from
the full private Cosmos runtime. Read
[`genesis_engine/engine/README.md`](genesis_engine/engine/README.md) before
using them; they are not required for Genesis or for `train_your_own.py`.
## Public measurement data
[`data/quantum_measurements_public.jsonl`](data/quantum_measurements_public.jsonl)
is a privacy-filtered release of the measurement archive:
- **7,770,112** samples in **1,897** explicitly backend-labeled IBM Quantum
hardware job records.
- **3,584,000** samples in **877** legacy records whose provider was not
retained. They remain useful distributional data but are **not counted as
verified hardware provenance**.
- **1,024** samples in **2** Azure `rigetti.sim.qvm` records, explicitly
labeled as classical simulator output.
Raw `physics` payloads were deliberately removed because some contain derived
sensory, biometric, or private runtime state. Counts, backend labels, job IDs,
timestamps, and shot totals are retained. See
[`data/README.md`](data/README.md) and the machine-readable data manifest.
The paired sensory-conditioning benchmark publishes aggregate metrics, hashes,
field names, and provenance in
`benchmarks/results/paired_conditioning_20260730.json`; private turn text and
the author's paired dataset are not distributed.
## What is not included
- API keys, OAuth tokens, cloud connection strings, account cookies, or private
vault material.
- The author's conversations, long-term memory, camera/audio captures, raw
sensory telemetry, or private paired text dataset.
- The author's large conversational Cosmos weights.
- Any claim that quantum entropy improves model accuracy.
- Any claim about machine consciousness.
## Licensing
This is a mixed-license release. Research documents, measurement data,
benchmarks, and the quantum-born model are released under CC BY 4.0. The
Genesis framework has the personal-use/proprietary terms in
`genesis_engine/LICENSE`. See [`LICENSE.md`](LICENSE.md) before redistributing
or building a product.
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