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

File size: 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
}