Instructions to use AeroAIAviation/Folio-1 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/Folio-1 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/Folio-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AeroAIAviation/Folio-1: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/Folio-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AeroAIAviation/Folio-1: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/Folio-1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AeroAIAviation/Folio-1: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/Folio-1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AeroAIAviation/Folio-1:Q4_K_M

Use Docker

docker model run hf.co/AeroAIAviation/Folio-1:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use AeroAIAviation/Folio-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AeroAIAviation/Folio-1"
# 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/Folio-1",
		"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/AeroAIAviation/Folio-1:Q4_K_M

Ollama
How to use AeroAIAviation/Folio-1 with Ollama:
```
ollama run hf.co/AeroAIAviation/Folio-1:Q4_K_M
```
Unsloth Desktop

How to use AeroAIAviation/Folio-1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AeroAIAviation/Folio-1: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/Folio-1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AeroAIAviation/Folio-1 with Docker Model Runner:
```
docker model run hf.co/AeroAIAviation/Folio-1:Q4_K_M
```

Lemonade

How to use AeroAIAviation/Folio-1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AeroAIAviation/Folio-1:Q4_K_M

Run and chat with the model

lemonade run user.Folio-1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AeroAIAviation/Folio-1 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/Folio-1: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/Folio-1:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AeroAIAviation/Folio-1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AeroAIAviation/Folio-1: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/Folio-1: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"

Introducing the first coding model (1.5B), in joint development between Foundation and AeroAI

Trained on Foundation's Forge on MLX

Available in Q4_K_M GGUF

Intended Use

Coding and developmental work.

Limitations and Safety

Not for commercial and/or operational use. Foundation's Folio-1 is experimental and may produce incorrect, outdated, or fabricated answers.

Do not use it for commerical or cyber work.
Small models may hallucinate, particularly on numeric and regulatory detail.

Built in development between Foundation and AeroAI.

Quick Start - Ollama

ollama run hf.co/AeroAIAviation/Folio-1:Q4_K_M

Python (Transformers)

pip install -U transformers torch accelerate

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AeroAI/Folio-1"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [{"role": "user", "content": "Code me a single file website. You decide the context and domain."}]
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/Folio-1 --prompt "Explain Terminal shell commands and what they are." --max-tokens 256

Model Details

Field	Value
Developer	Foundation + AeroAI
Family	Folio
Parameters	1.5B
Type	Causal language model (chat)
Domain	Coding and developmental
Input / Output	Text and image/ Text
Context length	32,000
Training data	AeroAI x Foundation Proprietary + Distillation
Fine-tuning method	LoRa + FFT
License	Apache 2.0

Benchmarks and performance (coding)

Benchmark	Score (%)
HumanEval	43.9
HumanEval+	36.6
MBPP	69.2
MBPP+	58.6
MBPP (3-shot)	59.2
BigCodeBench-Complete (Full)	34.6
BigCodeBench-Complete (Hard)	9.5

Training

Folio-1 was trained on Foundation's FORGE The model was trained in four stages:

Base model testing.
Model training.
Model testing.
Post training/fine tuning and alignment.

Training materials included AeroAI X Foundation's proprietary datasets, along with distilled datasets using Perplexity, OpenAI's ChatGPT, Oscilla, and Anthropic's Claude.

Citation

@misc{aeroai_Folio-1,
  title  = {Folio-1: An Experimental Coding and Developmental work model},
  author = {{AeroAI}},
  year   = {2026}
}

Contact/Support

Website: https://huggingface.co/AeroAIAviation/Folio-1 https://foundationai-iota.vercel.app/models Email: aeroaiaviation@icloud.com or aeroaisupport@icloud.com

Downloads last month: -

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for AeroAIAviation/Folio-1

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Quantized

(56)

this model