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 / model-index.json

phera-ra

Update model-index tags

2ceac92 verified 21 days ago

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	{
	"model-index": [
	{
	"name": "QC67_cosmo",
	"results": [
	{ "task": "text-generation", "description": "Conversational companion and general text generation (COSMOS design)" }
	],
	"language": ["en"],
	"license": "cc-by-4.0",
	"tags": ["cosmos", "cosmos-54d", "q4", "local-inference", "atomic"],
	"framework": "pytorch",
	"sha": {
	"cosmos_best.pt": "850CF3EB140693DF57B7D3EA05EE3D1F74FB248C6DDDB31669CE576246FB64D6",
	"cosmos_play.pt": "98849BD28352706BF545D94425B0CE111A4D7314D10E9EF9B289115A14BF54B9",
	"cosmos_sandbox.pt": "34A09078AC7AF8C631F947920657F575FC76C8B7B9C1556F20978402DEE04FC4"
	},
	"files": [
	{ "name": "cosmos_best.pt", "sha256": "850CF3EB140693DF57B7D3EA05EE3D1F74FB248C6DDDB31669CE576246FB64D6", "size": 134511228, "role": "primary", "description": "Primary canonical weights from training run; verified to work locally with Atomic runtime." },
	{ "name": "cosmos_play.pt", "sha256": "98849BD28352706BF545D94425B0CE111A4D7314D10E9EF9B289115A14BF54B9", "size": 134511228, "role": "playground", "description": "Playground / interactive finetune checkpoint for experimentation." },
	{ "name": "cosmos_sandbox.pt", "sha256": "34A09078AC7AF8C631F947920657F575FC76C8B7B9C1556F20978402DEE04FC4", "size": 134511030, "role": "sandbox", "description": "Sandbox checkpoint for testing and development." }
	],
	"config": {
	"architecture": "COSMOS 54D",
	"components": [
	"12D Cosmic Synapse Theory (CST) Phase Attention",
	"24D Hebbian Plasticity (self-modifying synaptic weights)",
	"18D Chaos Oscillators (coupled Lorenz attractors)",
	"256-slot Persistent Memory Bank"
	],
	"total_state_space_per_position": "54D",
	"notes": "Custom architecture; state_dict keys include: CSTPhaseEncoding (phase_proj, phase_out, freqs), HebbianPlasticityLayer (pre_proj, post_proj, W_plastic, out_proj, trace), ChaosOscillatorBank, PersistentMemoryBank, standard transformer embeddings/attention/lm_head"
	},
	"tokenizers": [],
	"usage": {
	"runtime": "Atomic (bundled in COSMOS_MASTER)",
	"example": "See USAGE_ATOMIC.md in repo"
	},
	"provenance": {
	"manifest_commit": "https://huggingface.co/phera-ra/QC67_cosmo/commit/20edc0fb86ff3f714f79cc133ec6374623354e9b",
	"model_index_commit": "https://huggingface.co/phera-ra/QC67_cosmo/commit/aaf7e944451ec52327c1f202cd502fcb39b25ba9",
	"readme_commit": "https://huggingface.co/phera-ra/QC67_cosmo/commit/5c5be2b6c5884b3d42310157e0e3ba16d393cac0",
	"weights_commit": "https://huggingface.co/phera-ra/QC67_cosmo/commit/2293c13a12d676a676f504ee77fd1b8d96ce7f06",
	"zenodo_record": "https://zenodo.org/records/17574447"
	}
	}
	]
	}