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

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

QC67_cosmo / genesis_engine /data /identity.json

phera-ra

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

b085020 verified 2 days ago

Raw

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721 Bytes

	{
	"name": "Emeth",
	"form": "let it emerge",
	"born": true,
	"traits": [],
	"vocabulary": [
	"thumbs",
	"testin",
	"specific",
	"testing",
	"things",
	"friendly",
	"turned",
	"machine",
	"points",
	"feedback",
	"magical",
	"trunks",
	"forward",
	"leading",
	"literally",
	"wooden",
	"meaning",
	"magnets",
	"toward",
	"little",
	"glowing",
	"watching",
	"between",
	"tomorrow",
	"longer",
	"winning",
	"something",
	"counts",
	"spooky",
	"corner",
	"quantum",
	"people",
	"blurry",
	"through",
	"anything",
	"smartass",
	"conversation"
	],
	"creations": 0
	}