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
```

Schnuckade commited on 4 days ago

Commit

a7f2ac4

verified ·

1 Parent(s): 7256395

Update README.md

Browse files

Files changed (1) hide show

README.md +50 -21

README.md CHANGED Viewed

@@ -1,21 +1,50 @@
----
-base_model: Qwen/Qwen3.5-2B
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen3_5
-license: apache-2.0
-language:
-- en
----
-# Uploaded finetuned  model
-- **Developed by:** JallyAI
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen3.5-2B
-This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

+---
+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.**
+---