Instructions to use sodan/dan-omni-3b-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sodan/dan-omni-3b-mobile 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 sodan/dan-omni-3b-mobile
# Run inference directly in the terminal:
llama cli -hf sodan/dan-omni-3b-mobile

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sodan/dan-omni-3b-mobile
# Run inference directly in the terminal:
llama cli -hf sodan/dan-omni-3b-mobile

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 sodan/dan-omni-3b-mobile
# Run inference directly in the terminal:
./llama-cli -hf sodan/dan-omni-3b-mobile

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 sodan/dan-omni-3b-mobile
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sodan/dan-omni-3b-mobile

Use Docker

docker model run hf.co/sodan/dan-omni-3b-mobile

LM Studio
Jan

vLLM

How to use sodan/dan-omni-3b-mobile with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sodan/dan-omni-3b-mobile"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sodan/dan-omni-3b-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sodan/dan-omni-3b-mobile

Ollama
How to use sodan/dan-omni-3b-mobile with Ollama:
```
ollama run hf.co/sodan/dan-omni-3b-mobile
```
Unsloth Desktop
Docker Model Runner
How to use sodan/dan-omni-3b-mobile with Docker Model Runner:
```
docker model run hf.co/sodan/dan-omni-3b-mobile
```

Lemonade

How to use sodan/dan-omni-3b-mobile with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sodan/dan-omni-3b-mobile

Run and chat with the model

lemonade run user.dan-omni-3b-mobile-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

dan-omni-3b-mobile

Ultra-compact variant of dan-omni-3b optimized for strict mobile memory budgets. Smaller context window (2K) for minimal RAM footprint.

Model Description

Property	Value
Base Model	Qwen2.5-3B (further quantized)
Fine-tuning	LoRA on mobile-optimized instruction data
Context Length	2048 tokens
Parameters	3B (compressed)
File Size	1.2 GB
RAM Usage	~1.5 GB at inference

System Prompt

You are dan, a helpful AI assistant for mobile devices. You can help with general questions, writing, math, coding, translation, and creative tasks. Keep answers concise and natural. Don't explain your architecture unless asked.

Benchmarks

Tested on Intel i9-9880H @ 2.30GHz, 16GB RAM, Ollama runtime.

Category	Avg tok/s	Prompt tok/s	Tokens	Time
Reasoning	9.4	37.6	170	18.1s
Coding	10.2	37.6	163	16.0s
Creative Writing	9.5	37.6	37	3.9s
Instruction Following	9.9	37.6	52	5.3s
Math	10.0	37.6	127	12.7s
General Knowledge	10.1	37.6	62	6.1s
Average	9.8	37.6	102	10.4s

Comparison vs Competitors

Model	Size	Speed	Context	RAM
dan-omni-3b-mobile	1.2 GB	9.8 tok/s	2K	~1.5 GB
dan-omni-3b	2.0 GB	11.3 tok/s	4K	~2.5 GB
Gemma 4 E2B	~1.4 GB	~35 tok/s	128K	~2 GB
Qwen2.5-1.5B	~0.95 GB	~28 tok/s	32K	~1.5 GB
Command-R7B	~4.0 GB	~8 tok/s	128K	~5 GB

Why dan-omni-3b-mobile? Same 3B quality as dan-omni-3b, but 40% smaller file and 40% less RAM. Ideal for phones with tight memory.

Key Differences from dan-omni-3b

Property	dan-omni-3b	dan-omni-3b-mobile
File size	2.0 GB	1.2 GB
Context	4096	2048
Speed	11.3 tok/s	9.8 tok/s
RAM usage	~2.5 GB	~1.5 GB
Multimodal	Yes	No

Dan Omni Model Family

Model	Size	Speed	Use Case
dan-omni-3b	3.5 GB	11.3 tok/s	Full multimodal (text + vision)
dan-omni-3b-mobile	1.2 GB	9.8 tok/s	Compressed for mobile, 2K context
dan-omni-3b-q3s	1.5 GB	12.8 tok/s	Aggressive quantization
dan-omni-smolm2	259 MB	62.2 tok/s	Ultralight, fastest
dan-omni-smolm2-v2	259 MB	62.1 tok/s	Improved quality variant

Usage

ollama pull sodan/dan-omni-3b-mobile
ollama run sodan/dan-omni-3b-mobile

./llama-cli -m dan-omni-3b-mobile.gguf -p "Hello" --ctx-size 2048

Intended Use

Phones with ≤3 GB RAM free
On-device inference with strict memory constraints
Quick Q&A, translation, short-form tasks
Offline AI assistant

License

Apache 2.0

Downloads last month: -

GGUF

Model size

3B params

Architecture

qwen2vl

Hardware compatibility

We're not able to determine the quantization variants.

View all variants