Instructions to use FoundationML/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 FoundationML/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 FoundationML/Folio-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FoundationML/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 FoundationML/Folio-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FoundationML/Folio-1:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf FoundationML/Folio-1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf FoundationML/Folio-1:Q4_K_M

Use Docker

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

Llama new
LM Studio
Jan

vLLM

How to use FoundationML/Folio-1 with vLLM:

Install from pip and serve model

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

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

How to use FoundationML/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 FoundationML/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": "FoundationML/Folio-1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use FoundationML/Folio-1 with Lemonade:

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use FoundationML/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 FoundationML/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 "FoundationML/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"

Folio-1 / README.md

FoundationML

Upload folder using huggingface_hub

ece4eac verified 12 days ago

preview code

Raw History Blame Contribute Delete

3.58 kB

---
tags:
- Coding
- Developmental
- Multimodal
- GGUF
- Q_4_KM
- Foundation
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-1.5B
pipeline_tag: image-text-to-text
---

![Folio-1](folio.png)


![Status](https://img.shields.io/badge/status-multimodal-blue) ![Params](https://img.shields.io/badge/parameters-1.5b-blue) ![Status](https://img.shields.io/badge/company-Foundation-black) 


# 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**


<video controls autoplay muted loop playsinline width="720">
  <source src="https://huggingface.co/AeroAIAviation/Folio-1/resolve/main/video.MP4" type="video/mp4">
</video>



## 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.
![foundation](foundationfolio.JPG)


## Quick Start - Ollama 

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

### Python (Transformers)


```bash
pip install -U transformers torch accelerate
```

```python
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)

```bash
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

![Foundation](foliodesign.png)

| 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: 
1. Base model testing.
2. Model training.
3. Model testing.
4. 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

```bibtex
@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

	---
	tags:
	- Coding
	- Developmental
	- Multimodal
	- GGUF
	- Q_4_KM
	- Foundation
	license: apache-2.0
	language:
	- en
	base_model:
	- Qwen/Qwen2.5-Coder-1.5B
	pipeline_tag: image-text-to-text
	---


	![Folio-1](folio.png)


	![Status](https://img.shields.io/badge/status-multimodal-blue) ![Params](https://img.shields.io/badge/parameters-1.5b-blue) ![Status](https://img.shields.io/badge/company-Foundation-black)


	# 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


	<video controls autoplay muted loop playsinline width="720">
	<source src="https://huggingface.co/AeroAIAviation/Folio-1/resolve/main/video.MP4" type="video/mp4">
	</video>



	## 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.
	![foundation](foundationfolio.JPG)


	## Quick Start - Ollama

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

	### Python (Transformers)


	```bash
	pip install -U transformers torch accelerate
	```

	```python
	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)

	```bash
	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

	![Foundation](foliodesign.png)

	\| 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:
	1. Base model testing.
	2. Model training.
	3. Model testing.
	4. 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

	```bibtex
	@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