Instructions to use NoriAI/NoriFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NoriAI/NoriFlash 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 NoriAI/NoriFlash:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NoriAI/NoriFlash:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NoriAI/NoriFlash:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NoriAI/NoriFlash:Q4_K_M

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 NoriAI/NoriFlash:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NoriAI/NoriFlash:Q4_K_M

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 NoriAI/NoriFlash:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NoriAI/NoriFlash:Q4_K_M

Use Docker

docker model run hf.co/NoriAI/NoriFlash:Q4_K_M

LM Studio
Jan

vLLM

How to use NoriAI/NoriFlash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NoriAI/NoriFlash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NoriAI/NoriFlash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NoriAI/NoriFlash:Q4_K_M

Ollama
How to use NoriAI/NoriFlash with Ollama:
```
ollama run hf.co/NoriAI/NoriFlash:Q4_K_M
```

Unsloth Studio

How to use NoriAI/NoriFlash 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 NoriAI/NoriFlash 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 NoriAI/NoriFlash to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NoriAI/NoriFlash to start chatting

How to use NoriAI/NoriFlash with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NoriAI/NoriFlash:Q4_K_M

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": "NoriAI/NoriFlash:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NoriAI/NoriFlash with Docker Model Runner:
```
docker model run hf.co/NoriAI/NoriFlash:Q4_K_M
```

Lemonade

How to use NoriAI/NoriFlash with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NoriAI/NoriFlash:Q4_K_M

Run and chat with the model

lemonade run user.NoriFlash-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NoriAI/NoriFlash with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NoriAI/NoriFlash:Q4_K_M

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 NoriAI/NoriFlash:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NoriAI/NoriFlash with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NoriAI/NoriFlash:Q4_K_M

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 "NoriAI/NoriFlash:Q4_K_M" \
  --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"

✨ Nori 1.5B ✨

Компактный и дружелюбный ИИ-ассистент

Быстрая, лёгкая и отзывчивая языковая модель, дообученная для естественного диалога

🌟 О модели

Nori 1.5B — компактная разговорная модель, дообученная (fine-tuned) с использованием Unsloth поверх открытой архитектуры Qwen2.5-1.5B-Instruct от Alibaba Cloud.

📦 Доступные квантования

Файл	Квантование	Размер	Рекомендуется для
`qwen2.5-1.5b-instruct.Q4_K_M.gguf`	Q4_K_M	~1.0 GB	CPU / слабое железо
`qwen2.5-1.5b-instruct.Q8_0.gguf`	Q8_0	~1.6 GB	Баланс качества и скорости

🚀 Быстрый старт (llama.cpp)

./llama-cli --model qwen2.5-1.5b-instruct.Q4_K_M.gguf -p "Привет, как тебя зовут?"

🐍 Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="qwen2.5-1.5b-instruct.Q4_K_M.gguf", n_ctx=2048)
output = llm("Привет! Расскажи о себе.", max_tokens=128)
print(output["choices"][0]["text"])

🦙 Ollama

ollama create nori -f Modelfile
ollama run nori

🏗️ База и метод обучения

Базовая модель: Qwen2.5-1.5B-Instruct (Alibaba Cloud)
Метод: LoRA fine-tuning через Unsloth
Формат: GGUF (llama.cpp, LM Studio, Ollama, koboldcpp)

📄 Лицензия

Apache 2.0 — соответствует лицензии базовой модели Qwen2.5.

Made with 💛 using Unsloth

Downloads last month: 54

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

Model tree for NoriAI/NoriFlash

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(261)

this model