Instructions to use ReallyFloppyPenguin/Jan-nano-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ReallyFloppyPenguin/Jan-nano-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ReallyFloppyPenguin/Jan-nano-GGUF", filename="model-f16.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 ReallyFloppyPenguin/Jan-nano-GGUF 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 ReallyFloppyPenguin/Jan-nano-GGUF:F16 # Run inference directly in the terminal: llama cli -hf ReallyFloppyPenguin/Jan-nano-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ReallyFloppyPenguin/Jan-nano-GGUF:F16 # Run inference directly in the terminal: llama cli -hf ReallyFloppyPenguin/Jan-nano-GGUF:F16
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 ReallyFloppyPenguin/Jan-nano-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf ReallyFloppyPenguin/Jan-nano-GGUF:F16
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 ReallyFloppyPenguin/Jan-nano-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ReallyFloppyPenguin/Jan-nano-GGUF:F16
Use Docker
docker model run hf.co/ReallyFloppyPenguin/Jan-nano-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use ReallyFloppyPenguin/Jan-nano-GGUF with Ollama:
ollama run hf.co/ReallyFloppyPenguin/Jan-nano-GGUF:F16
- Unsloth Studio
How to use ReallyFloppyPenguin/Jan-nano-GGUF 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 ReallyFloppyPenguin/Jan-nano-GGUF 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 ReallyFloppyPenguin/Jan-nano-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ReallyFloppyPenguin/Jan-nano-GGUF to start chatting
- Pi
How to use ReallyFloppyPenguin/Jan-nano-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ReallyFloppyPenguin/Jan-nano-GGUF:F16
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": "ReallyFloppyPenguin/Jan-nano-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ReallyFloppyPenguin/Jan-nano-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ReallyFloppyPenguin/Jan-nano-GGUF:F16
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 ReallyFloppyPenguin/Jan-nano-GGUF:F16
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use ReallyFloppyPenguin/Jan-nano-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ReallyFloppyPenguin/Jan-nano-GGUF:F16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ReallyFloppyPenguin/Jan-nano-GGUF:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use ReallyFloppyPenguin/Jan-nano-GGUF with Docker Model Runner:
docker model run hf.co/ReallyFloppyPenguin/Jan-nano-GGUF:F16
- Lemonade
How to use ReallyFloppyPenguin/Jan-nano-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ReallyFloppyPenguin/Jan-nano-GGUF:F16
Run and chat with the model
lemonade run user.Jan-nano-GGUF-F16
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
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---
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language:
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- en
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library_name: gguf
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base_model: Menlo/Jan-nano
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tags:
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- gguf
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- quantized
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- llama.cpp
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license: apache-2.0
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---
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# Menlo/Jan-nano - GGUF
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This repository contains GGUF quantizations of [Menlo/Jan-nano](https://huggingface.co/Menlo/Jan-nano).
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## About GGUF
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GGUF is a quantization method that allows you to run large language models on consumer hardware by reducing the precision of the model weights.
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## Files
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| model-f16.gguf | f16 | Large | Original precision |
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| model-q4_0.gguf | Q4_0 | Small | 4-bit quantization |
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| model-q4_1.gguf | Q4_1 | Small | 4-bit quantization (higher quality) |
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| model-q5_0.gguf | Q5_0 | Medium | 5-bit quantization |
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| model-q5_1.gguf | Q5_1 | Medium | 5-bit quantization (higher quality) |
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| model-q8_0.gguf | Q8_0 | Large | 8-bit quantization |
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## Usage
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You can use these models with llama.cpp or any other GGUF-compatible inference engine.
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### llama.cpp
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```bash
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./llama-cli -m model-q4_0.gguf -p "Your prompt here"
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```
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### Python (using llama-cpp-python)
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="model-q4_0.gguf")
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output = llm("Your prompt here", max_tokens=512)
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print(output['choices'][0]['text'])
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```
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## Original Model
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This is a quantized version of [Menlo/Jan-nano](https://huggingface.co/Menlo/Jan-nano). Please refer to the original model card for more information about the model's capabilities, training data, and usage guidelines.
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## Conversion Details
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- Converted using llama.cpp
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- Original model downloaded from Hugging Face
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- Multiple quantization levels provided for different use cases
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## License
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This model inherits the license from the original model. Please check the original model's license for usage terms.
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