Instructions to use JallyAI/Nomi-2-Mini-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JallyAI/Nomi-2-Mini-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="JallyAI/Nomi-2-Mini-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("JallyAI/Nomi-2-Mini-GGUF")
model = AutoModelForMultimodalLM.from_pretrained("JallyAI/Nomi-2-Mini-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use JallyAI/Nomi-2-Mini-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 JallyAI/Nomi-2-Mini-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf JallyAI/Nomi-2-Mini-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf JallyAI/Nomi-2-Mini-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf JallyAI/Nomi-2-Mini-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 JallyAI/Nomi-2-Mini-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf JallyAI/Nomi-2-Mini-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 JallyAI/Nomi-2-Mini-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JallyAI/Nomi-2-Mini-GGUF:F16

Use Docker

docker model run hf.co/JallyAI/Nomi-2-Mini-GGUF:F16

LM Studio
Jan

vLLM

How to use JallyAI/Nomi-2-Mini-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JallyAI/Nomi-2-Mini-GGUF"
# 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-2-Mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/JallyAI/Nomi-2-Mini-GGUF:F16

SGLang

How to use JallyAI/Nomi-2-Mini-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "JallyAI/Nomi-2-Mini-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JallyAI/Nomi-2-Mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "JallyAI/Nomi-2-Mini-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JallyAI/Nomi-2-Mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use JallyAI/Nomi-2-Mini-GGUF with Ollama:
```
ollama run hf.co/JallyAI/Nomi-2-Mini-GGUF:F16
```
Unsloth Desktop

How to use JallyAI/Nomi-2-Mini-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JallyAI/Nomi-2-Mini-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-2-Mini-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use JallyAI/Nomi-2-Mini-GGUF with Docker Model Runner:
```
docker model run hf.co/JallyAI/Nomi-2-Mini-GGUF:F16
```

Lemonade

How to use JallyAI/Nomi-2-Mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JallyAI/Nomi-2-Mini-GGUF:F16

Run and chat with the model

lemonade run user.Nomi-2-Mini-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use JallyAI/Nomi-2-Mini-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 JallyAI/Nomi-2-Mini-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 JallyAI/Nomi-2-Mini-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use JallyAI/Nomi-2-Mini-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JallyAI/Nomi-2-Mini-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 "JallyAI/Nomi-2-Mini-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"

Schnuckade commited on Aug 15

Commit

4db12c8

verified ·

1 Parent(s): d8f1214

Add README

Browse files

Files changed (1) hide show

README.md +13 -42

README.md CHANGED Viewed

@@ -1,50 +1,21 @@
 ---
-license: apache-2.0
-base_model:
-- JallyAI/Nomi-2-Mini
-pipeline_tag: image-text-to-text
-library_name: transformers
 tags:
-- efficient
-- qwen
-- qwen3.5
-- nomi
-- lazyloopstudio
 - unsloth
-- nomi2
 ---
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/6921fa6332f7fb129563d495/aR36SrpWzksbcGbcp84pE.png" width="128">
-</p>
-# Nomi 2.0 Mini
-## Introduction
-Introducing **Nomi 2 Mini**, it was fine tuned on the same data as Nomi 2 and has a very short and efficient reasoning thanks to the RASV reasoning style. Nomi 2 Mini has only 2B parameters, half the parameters of the normal Nomi 2.
-If you want to know more about Nomi 2 or RASV, checkout the Nomi 2 model card
- https://huggingface.com/JallyAI/Nomi-2
-## 🌟 Key Features & Improvements
-* **Architecture:** Qwen-3.5-2B (requires just ~1.5 GB VRAM).
-* **Multilingual Support:** Can understand and generate text English and many other languages.
-* **Efficiency:** Get 100+ tokens/s on consumer hardware, like an RTX 4060. You can use Nomi 2 Mini with an context window of almost 200k tokens
-## 🧠 Training Details
-* **Base Model:** `Qwen/Qwen3.5-2B`
-* **Fine-tuning:** SFT (Supervised Fine-Tuning).
-* **Training Tool:** **Unsloth** (for 4-bit optimized training).
-## 😎 Cool License
-**Feel free to use or improve Nomi! Benchmark results are always welcome.**
----

 ---
 tags:
+- gguf
+- llama.cpp
 - unsloth
+- vision-language-model
 ---
+# Nomi-2-Mini-GGUF : GGUF
+This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+**Example usage**:
+- For text only LLMs:    `llama-cli -hf JallyAI/Nomi-2-Mini-GGUF --jinja`
+- For multimodal models: `llama-mtmd-cli -hf JallyAI/Nomi-2-Mini-GGUF --jinja`
+## Available Model files:
+- `Qwen3.5-2B.Q4_K_M.gguf`
+- `Qwen3.5-2B.F16-mmproj.gguf`
+This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)