Instructions to use gustavokuklinski/aeon-360M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gustavokuklinski/aeon-360M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="gustavokuklinski/aeon-360M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gustavokuklinski/aeon-360M")
model = AutoModelForCausalLM.from_pretrained("gustavokuklinski/aeon-360M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gustavokuklinski/aeon-360M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gustavokuklinski/aeon-360M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gustavokuklinski/aeon-360M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gustavokuklinski/aeon-360M

SGLang

How to use gustavokuklinski/aeon-360M 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 "gustavokuklinski/aeon-360M" \
    --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": "gustavokuklinski/aeon-360M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "gustavokuklinski/aeon-360M" \
        --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": "gustavokuklinski/aeon-360M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gustavokuklinski/aeon-360M with Docker Model Runner:
```
docker model run hf.co/gustavokuklinski/aeon-360M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

AEON

AEON is portable, private, and capable of operating fully offline. It democratizes access to powerful, dynamic AI capabilities for a wider audience, regardless of their hardware.

The finetuned model was build to be like a "friend" for RAG personal files and work with insights.

Docs

Page aeon.ai
Github Project: AEON.ai
Github LLM Finetune Scripts: AEON.llm

Using Aeon

AEON uses Python with virtual environment and git lfs installed.

/$ git lfs install

# With plugins
/$ git clone --recurse-submodules https://github.com/gustavokuklinski/aeon.ai.git

# Without plugins
/$ git clone https://github.com/gustavokuklinski/aeon.ai.git

# Create .venv
/$ python -m venv .venv

# Start virtual env
/$ source .venv/bin/activate

# Run check and install dependencies
/$ python3 scripts/install.py 

# Start AEON
/$ python3 aeon.py

Using Docker

docker build -t aeon .

docker run -it --rm -p 7860:7860 -v "$(pwd):/app" aeon

SFT

Tested on

OS	CPU	GPU	RAM
Ubuntu 24.04.2 LTS	Intel i7-10510U	Intel CometLake-U GT2	16GB
Windows 11 Home Edition	Intel i7-10510U	Intel CometLake-U GT2	8GB

Downloads last month: 11

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for gustavokuklinski/aeon-360M

Base model

HuggingFaceTB/SmolLM2-360M

Finetuned

(100)

this model

Quantizations

3 models

Datasets used to train gustavokuklinski/aeon-360M

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