Instructions to use MoonieJeon/COSMOS-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MoonieJeon/COSMOS-3B with Cosmos:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

llama-cpp-python

How to use MoonieJeon/COSMOS-3B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MoonieJeon/COSMOS-3B",
	filename="cosmos-3b.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MoonieJeon/COSMOS-3B 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 MoonieJeon/COSMOS-3B
# Run inference directly in the terminal:
llama cli -hf MoonieJeon/COSMOS-3B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MoonieJeon/COSMOS-3B
# Run inference directly in the terminal:
llama cli -hf MoonieJeon/COSMOS-3B

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 MoonieJeon/COSMOS-3B
# Run inference directly in the terminal:
./llama-cli -hf MoonieJeon/COSMOS-3B

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 MoonieJeon/COSMOS-3B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MoonieJeon/COSMOS-3B

Use Docker

docker model run hf.co/MoonieJeon/COSMOS-3B

LM Studio
Jan
Ollama
How to use MoonieJeon/COSMOS-3B with Ollama:
```
ollama run hf.co/MoonieJeon/COSMOS-3B
```

Unsloth Studio

How to use MoonieJeon/COSMOS-3B 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 MoonieJeon/COSMOS-3B 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 MoonieJeon/COSMOS-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MoonieJeon/COSMOS-3B to start chatting

Atomic Chat new
Docker Model Runner
How to use MoonieJeon/COSMOS-3B with Docker Model Runner:
```
docker model run hf.co/MoonieJeon/COSMOS-3B
```

Lemonade

How to use MoonieJeon/COSMOS-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MoonieJeon/COSMOS-3B

Run and chat with the model

lemonade run user.COSMOS-3B-{{QUANT_TAG}}

List all available models

lemonade list

COSMOS-3B / README.md

MoonieJeon

Update README.md

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	---
	license: mit
	base_model: microsoft/Phi-3-mini-4k-instruct
	tags:
	- phi3
	- cosmos
	- local-ai
	- sovereign-ai
	- gguf
	language:
	- fr
	- en
	- mg
	---

	# COSMOS-3B

	COSMOS-3B is a sovereign language model optimized for structured writing and professional assistance. It is designed for seamless offline execution, ensuring complete data privacy and sovereignty.

	## Technical Specifications
	- Base Model: Phi-3-Mini (3.8B)
	- Quantization: Q4_K_M
	- Format: GGUF (Optimized for LLamaSharp)
	- Primary Goal: Structured content generation (AIDA framework).

	## Why COSMOS-3B?
	COSMOS-3B is the core inference engine of the Aether Project, built to address three critical needs:
	1. Resilience: Fully operational without internet dependency.
	2. Privacy: On-device processing ensures that user data never leaves the local environment.
	3. Interoperability: Standardized GGUF format for cross-platform compatibility.

	Compatibility

	COSMOS-3B is provided in GGUF format.
	It is compatible with all standard GGUF-compliant inference engines, including (but not limited to) LLamaSharp (C#), llama.cpp (C++), and various PHP-based LLM frameworks.

	## License
	This model is distributed under the MIT license. This ensures maximum flexibility and transparency for your projects.