Instructions to use CorelynAI/NeoMini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use CorelynAI/NeoMini with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="CorelynAI/NeoMini", filename="NeoMini_3B.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use CorelynAI/NeoMini with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CorelynAI/NeoMini # Run inference directly in the terminal: llama-cli -hf CorelynAI/NeoMini
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CorelynAI/NeoMini # Run inference directly in the terminal: llama-cli -hf CorelynAI/NeoMini
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 CorelynAI/NeoMini # Run inference directly in the terminal: ./llama-cli -hf CorelynAI/NeoMini
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 CorelynAI/NeoMini # Run inference directly in the terminal: ./build/bin/llama-cli -hf CorelynAI/NeoMini
Use Docker
docker model run hf.co/CorelynAI/NeoMini
- LM Studio
- Jan
- vLLM
How to use CorelynAI/NeoMini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CorelynAI/NeoMini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CorelynAI/NeoMini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CorelynAI/NeoMini
- Ollama
How to use CorelynAI/NeoMini with Ollama:
ollama run hf.co/CorelynAI/NeoMini
- Unsloth Studio
How to use CorelynAI/NeoMini 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 CorelynAI/NeoMini 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 CorelynAI/NeoMini to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for CorelynAI/NeoMini to start chatting
- Pi
How to use CorelynAI/NeoMini with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf CorelynAI/NeoMini
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "CorelynAI/NeoMini" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use CorelynAI/NeoMini with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf CorelynAI/NeoMini
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default CorelynAI/NeoMini
Run Hermes
hermes
- Docker Model Runner
How to use CorelynAI/NeoMini with Docker Model Runner:
docker model run hf.co/CorelynAI/NeoMini
- Lemonade
How to use CorelynAI/NeoMini with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CorelynAI/NeoMini
Run and chat with the model
lemonade run user.NeoMini-{{QUANT_TAG}}List all available models
lemonade list
| license: apache-2.0 | |
| tags: | |
| - text-generation | |
| - instruction-tuned | |
| - llama | |
| - gguf | |
| - chatbot | |
| library_name: llama.cpp | |
| language: en | |
| datasets: | |
| - custom | |
| model-index: | |
| - name: Corelyn NeoMini | |
| results: [] | |
| base_model: | |
| - mistralai/Ministral-3-3B-Base-2512 | |
|  | |
| # Corelyn NeoMini GGUF Model | |
| ## Specifications : | |
| - Model Name: Corelyn NeoMini | |
| - Base Name: NeoMini-3B | |
| - Type: Instruct / Fine-tuned | |
| - Architecture: Ministral-3 | |
| - Size: 3B parameters | |
| - Organization: Corelyn | |
| ## Model Overview | |
| Corelyn NeoMini is a 3-billion parameter LLaMA-based instruction-tuned model, designed for general-purpose assistant tasks and knowledge extraction. It is a fine-tuned variant optimized for instruction-following use cases. | |
| - Fine-tuning type: Instruct | |
| - Base architecture: Ministral-3 | |
| - Parameter count: 3B | |
| ### This model is suitable for applications such as: | |
| - Chatbots and conversational AI | |
| - Knowledge retrieval and Q&A | |
| - Code and text generation | |
| - Instruction-following tasks | |
| ## Usage | |
| Download from : [NeoMini3.2](https://huggingface.co/CorelynAI/NeoMini/resolve/main/NeoMini_3B.gguf) | |
| ```python | |
| # pip install pip install llama-cpp-python | |
| from llama_cpp import Llama | |
| # Load the model (update the path to where your .gguf file is) | |
| llm = Llama(model_path="path/to/the/file/NeoMini_3B.gguf") | |
| # Create chat completion | |
| response = llm.create_chat_completion( | |
| messages=[{"role": "user", "content": "Create a Haiku about AI"}] | |
| ) | |
| # Print the generated text | |
| print(response.choices[0].message["content"]) | |
| ``` |