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

Libraries

How to use JallyAI/Nomi-2-Mini 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")
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")
model = AutoModelForMultimodalLM.from_pretrained("JallyAI/Nomi-2-Mini", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JallyAI/Nomi-2-Mini 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"
# 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",
		"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

SGLang

How to use JallyAI/Nomi-2-Mini 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" \
    --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",
		"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" \
        --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",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use JallyAI/Nomi-2-Mini 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-2-Mini 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-2-Mini 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-2-Mini to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="JallyAI/Nomi-2-Mini",
    max_seq_length=2048,
)

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

Nomi-2-Mini

File size: 1,363 Bytes

a7f2ac4

---
license: apache-2.0
base_model:
- Qwen/Qwen3.5-2B
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.**

---