Instructions to use AeroAIAviation/Atlas-2-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AeroAIAviation/Atlas-2-Small 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 AeroAIAviation/Atlas-2-Small:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AeroAIAviation/Atlas-2-Small:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AeroAIAviation/Atlas-2-Small:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AeroAIAviation/Atlas-2-Small: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 AeroAIAviation/Atlas-2-Small:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AeroAIAviation/Atlas-2-Small: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 AeroAIAviation/Atlas-2-Small:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AeroAIAviation/Atlas-2-Small:Q4_K_M

Use Docker

docker model run hf.co/AeroAIAviation/Atlas-2-Small:Q4_K_M

LM Studio
Jan

vLLM

How to use AeroAIAviation/Atlas-2-Small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AeroAIAviation/Atlas-2-Small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AeroAIAviation/Atlas-2-Small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AeroAIAviation/Atlas-2-Small:Q4_K_M

Ollama
How to use AeroAIAviation/Atlas-2-Small with Ollama:
```
ollama run hf.co/AeroAIAviation/Atlas-2-Small:Q4_K_M
```
Unsloth Desktop

How to use AeroAIAviation/Atlas-2-Small with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AeroAIAviation/Atlas-2-Small:Q4_K_M

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": "AeroAIAviation/Atlas-2-Small:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AeroAIAviation/Atlas-2-Small with Docker Model Runner:
```
docker model run hf.co/AeroAIAviation/Atlas-2-Small:Q4_K_M
```

Lemonade

How to use AeroAIAviation/Atlas-2-Small with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AeroAIAviation/Atlas-2-Small:Q4_K_M

Run and chat with the model

lemonade run user.Atlas-2-Small-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AeroAIAviation/Atlas-2-Small with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AeroAIAviation/Atlas-2-Small: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 AeroAIAviation/Atlas-2-Small:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AeroAIAviation/Atlas-2-Small with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AeroAIAviation/Atlas-2-Small: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 "AeroAIAviation/Atlas-2-Small: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"

Atlas-2-Small

Experimental 2B-parameter language model from AeroAI for aviation question answering and chatbot-style queries.

Overview

Atlas-2-Small is the compact, experimental member of the Atlas-2 family. It is built for aviation Q&A and conversational use, and is small enough to run locally on consumer hardware.

Model	Role	Status
Atlas-2-Core	Fast, basic model	In development
Atlas-2-Pro	High-end thinking model	In development
Atlas-2-Small	Experimental 2B chatbot model	Experimental

Intended Use

Aviation question answering (general knowledge, regulations, procedures, terminology)
Chatbot-style conversational queries
Local experimentation, research, and prototyping

Limitations and Safety

Not for operational use. Atlas-2-Small is experimental and may produce incorrect, outdated, or fabricated answers.

Do not use it for flight planning, navigation, aircraft maintenance, or any safety-critical decision.
Always verify against official sources: FAA publications (14 CFR, AIM, POH/AFM), NOTAMs, and certified instructors.
Calculations (fuel, weight and balance, performance) must be independently checked.
Small models may hallucinate, particularly on numeric and regulatory detail.

Quick Start

Replace AeroAI/Atlas-2-Small with the actual repository or model path once published.

Requirements

pip install -U transformers torch accelerate

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AeroAI/Atlas-2-Small"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [{"role": "user", "content": "What does VFR stand for, and what are basic VFR weather minimums in Class E airspace below 10,000 ft MSL?"}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Apple Silicon (MLX)

pip install -U mlx-lm
mlx_lm.generate --model AeroAI/Atlas-2-Small --prompt "Explain density altitude." --max-tokens 256

Model Details

Field	Value
Developer	AeroAI
Family	Atlas-2
Parameters	2B
Type	Causal language model (chat)
Domain	Aviation
Input / Output	Text / Text
Context length	260,000
Training data	Proprietary + Distillation
Fine-tuning method	LoRa + FFT
License	TBD

Evaluation

Benchmark results (e.g., AvBench) will be added here.

Benchmark	Score
AvBench	TBD

System Prompt

See |systemprompt.txt| file under "Files and Versions" tab.

Citation

@misc{aeroai_atlas2small,
  title  = {Atlas-2-Small: An Experimental Aviation Chatbot Model},
  author = {{AeroAI}},
  year   = {2026}
}

Contact/Support

AeroAI - aeroaiaviation@icloud.com

Downloads last month: -

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

4-bit