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 Desktop
- 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
Commit History
-
Kit update 2026-08-06: samgo 5.7 (54D BPE, 59M), gate-init finding, Born-rule fix in quantum_pool, corpus-drift retraction + fingerprinting, frozen-corpus wiring table
Verified signatureAdds SAMGO.md and weights/samgo_weights.pt (59,353,668 params, GPT-2 BPE). FINDINGS.md: gate initialised at sigmoid(-4)=0.018 left the Hebbian kernel alive in 1 layer of 4 in every model ever trained here; opening it to 0.5 gives 4/4 and 0.054 nats, larger than the dyn12-vs-none gap. quantum_pool was flattening the measured Born-rule distribution to exactly uniform (3.4657 = log 32) and now preserves it (3.4598 vs true 3.4594). Retracts a 0.0645-nat wiring win that was the live corpus rewriting itself mid-experiment; adds corpus+flag fingerprinting to prevent recurrence.b085020
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Remove dead Modelfile: _Modelfile.cosmos_namebind
Verified signatureFROM D:\Cosmos\models\cosmos_namebind_merged -- a hardcoded local drive path that exists on no other machine. A working note, not distributable content. The namebind RESEARCH is kept: cosmos_merge_namebind.py (the LoRA merge recipe) and cosmos_namebind_probe.py (which tests whether the model self-labels as Qwen) are real experiments and remain in the repository.0a9c2fe
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Cosmos: lineage-first model card, full findings + benchmarks, Cosmic Spark server
Verified signatureCorrects earlier claims (not GGUF; 1.8M params not 2B; architecture is a standard pre-LN decoder, not Hebbian). Adds FINDINGS.md with every measurement including six null results and the instrument failures behind them. Adds benchmarks/ with runnable reproduction scripts for CHSH (S=2.7905), Lorenz constants, gate-matched topology, and integration structure. Adds spark_serve.py to run the weights over an Ollama-compatible API.d6da243