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 /engine /README.md

phera-ra

Release validated Cosmos kit with portable starter and public quantum archive

b8fadbf verified 9 days ago

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Advanced engine adapters

The Genesis companion does not need this directory. Its supported starter is genesis_engine/START_GENESIS.bat or python3 genesis_engine/genesis.py.

These scripts are retained for researchers integrating Genesis with a full Cosmos model checkout:

cosmos_54d_train_loop.py supervises bounded warm-start training when an experience corpus grows.
train_cosmos_from_play.py, train_cosmos_broadened.py, and train_12d_brain.py depend on the full Cosmos.web.cosmosynapse model package and are not standalone.
cosmos_prime_train.py, cosmos_merge_namebind.py, and related scripts can download third-party model weights and require PyTorch, Transformers, PEFT, and acceptance of the base model's license.

The 2026-07-31 continual learner adds:

a persistent last-trained corpus baseline
a cross-platform single-instance lock
parent-process and stop-file shutdown
bounded CPU/RAM safeguards
checkpoint modification verification before advancing the baseline

Expected full-runtime layout:

PROJECT/
  Cosmos/data/cosmos/experience_corpus.txt
  Cosmos/checkpoints/cosmos/cosmos_play.pt
  scripts/cosmos_54d_train_loop.py
  scripts/train_cosmos_from_play.py

The copy in this kit documents and preserves the adapter. Move it into that layout or adapt its path constants deliberately. Do not start it from the Genesis starter and expect it to discover a full model package.

For a standalone experiment use the top-level train_your_own.py, which contains the model and training harness it needs.