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
- 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 new
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 new
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
- 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
- Nomi 1.0-3b
- Introduction
- 🌟 Key Features & Improvements
- 🧠 Training Details
- 📝 Prompt Template (ChatML/Llama-3.2)
- 🛠️ Usage (Ollama)
- ⚠️ Limitations As a 3B parameter model, Nomi-1.0 is not a replacement for GPT-4 or large 70B models when it comes to deep world knowledge or complex mathematical reasoning. It is a specialized tool for speed, local privacy, and high-quality document structure.
- Introduction
Nomi 1.0-3b
Introduction
Nomi-1.0 is a refined mid-range Large Language Model based on the Llama-3.2-3B architecture. It was specifically developed to outperform standard 3B models in structured reporting, markdown formatting, and Python coding, making it an ideal assistant for local deployment on consumer hardware.
It is the first Model of the Nomi-Series
🌟 Key Features & Improvements
- Architecture: Llama-3.2-3B (Optimized for 8GB VRAM GPUs like RTX 4060).
- Formatting Master: Specifically trained to use H1, H2, tables, and bold text to make information instantly scannable.
- Coding Proficiency: Fine-tuned on the Magpie-Pro dataset to write cleaner Python code with built-in error handling (
try-except). - Multilingual Support: Excellent performance in both German and English.
- Efficiency: High-speed inference (~60+ tokens/sec) with a very low memory footprint.
🧠 Training Details
The goal of Nomi-1.0 was to create a "bridge" model that feels as smart as a 7B model but runs with the speed of a 3B model.
- Base Model:
unsloth/Llama-3.2-3B-Instruct-bnb-4bit - Fine-tuning: SFT (Supervised Fine-Tuning) using the Magpie-Pro dataset.
- Training Tool: Unsloth (for 4-bit optimized training).
- Optimization: High LoRA Rank (r=32) was used to ensure the model captures complex structural nuances.
📝 Prompt Template (ChatML/Llama-3.2)
For the best results in Ollama or LM Studio, use the following template:
<|start_header_id|>system<|end_header_id|>
You are Nomi-1.0, a high-performance 3B model. You provide superior, structured, and deep responses. Always use Markdown for clarity.<|eot_id|>
<|start_header_id|>user<|end_header_id|>
{Your Question}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
🛠️ Usage (Ollama)
- Download the
Nomi-1.0.gguf. - Create a Modelfile with the following content:
FROM ./Nomi-1.0.gguf
PARAMETER temperature 0.6
SYSTEM "You are Nomi-1.0, a high-performance 3B model. You provide superior, structured, and deep responses. Always use Markdown for clarity."
- Run the following command in your terminal:
ollama create Nomi-1.0 -f Modelfile
⚠️ Limitations As a 3B parameter model, Nomi-1.0 is not a replacement for GPT-4 or large 70B models when it comes to deep world knowledge or complex mathematical reasoning. It is a specialized tool for speed, local privacy, and high-quality document structure.
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Model tree for JallyAI/Nomi-1.0-3b
Base model
meta-llama/Llama-3.2-3B
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?" } ] }'