Instructions to use JallyAI/Nomi-1.0-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use JallyAI/Nomi-1.0-3b with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("JallyAI/Nomi-1.0-3b", set_active=True) - llama-cpp-python
How to use JallyAI/Nomi-1.0-3b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="JallyAI/Nomi-1.0-3b", filename="Nomi-1.0.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 JallyAI/Nomi-1.0-3b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf JallyAI/Nomi-1.0-3b # Run inference directly in the terminal: llama-cli -hf JallyAI/Nomi-1.0-3b
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf JallyAI/Nomi-1.0-3b # Run inference directly in the terminal: llama-cli -hf JallyAI/Nomi-1.0-3b
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 JallyAI/Nomi-1.0-3b # Run inference directly in the terminal: ./llama-cli -hf JallyAI/Nomi-1.0-3b
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 JallyAI/Nomi-1.0-3b # Run inference directly in the terminal: ./build/bin/llama-cli -hf JallyAI/Nomi-1.0-3b
Use Docker
docker model run hf.co/JallyAI/Nomi-1.0-3b
- LM Studio
- Jan
- vLLM
How to use JallyAI/Nomi-1.0-3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JallyAI/Nomi-1.0-3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JallyAI/Nomi-1.0-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JallyAI/Nomi-1.0-3b
- Ollama
How to use JallyAI/Nomi-1.0-3b with Ollama:
ollama run hf.co/JallyAI/Nomi-1.0-3b
- Unsloth Studio
How to use JallyAI/Nomi-1.0-3b 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 JallyAI/Nomi-1.0-3b 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 JallyAI/Nomi-1.0-3b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for JallyAI/Nomi-1.0-3b to start chatting
- Pi
How to use JallyAI/Nomi-1.0-3b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf JallyAI/Nomi-1.0-3b
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": "JallyAI/Nomi-1.0-3b" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use JallyAI/Nomi-1.0-3b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf JallyAI/Nomi-1.0-3b
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 JallyAI/Nomi-1.0-3b
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use JallyAI/Nomi-1.0-3b with Docker Model Runner:
docker model run hf.co/JallyAI/Nomi-1.0-3b
- Lemonade
How to use JallyAI/Nomi-1.0-3b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull JallyAI/Nomi-1.0-3b
Run and chat with the model
lemonade run user.Nomi-1.0-3b-{{QUANT_TAG}}List all available models
lemonade list
π’ Introducing Nomi-1.0-3b: A 3B Model That Thinks Like a 7B
Hey everyone! π
We just released Nomi-1.0-3b, a specialized mid-range LLM built on Llama-3.2-3B that punches way above its weight class.
π What makes Nomi special:
β‘ Speed meets quality: ~60+ tokens/sec with only 8GB VRAM (RTX 4060, Laptops)
π Formatting Master: Trained specifically for structured reports, markdown, and clean tables
π Coding Pro: Fine-tuned on Magpie-Pro to write Python with proper error handling
π Bilingual Excellence: Fluent in German & English
π Local-first: Perfect for privacy-focused deployments
The Goal: A "bridge" model that feels as intelligent as larger models but runs at 3B speeds.
Try it now:
Ollama/LM Studio Ready - Download the GGUF version
Works perfectly with standard Llama-3.2 chat templates
Apache 2.0 licensed - free to use & modify
π Model Card: LazyLoopStudio/Nomi-1.0-3b
This is the first model in our Nomi-Series, with more optimized variants coming. If you're building AI applications that need speed + quality on consumer hardware, give it a try!
Feedback, benchmarks, and suggestions are always welcome π