Instructions to use UUFO-Aigis/PicoLAiNN-100M-SmallDataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UUFO-Aigis/PicoLAiNN-100M-SmallDataset with Transformers:

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

pipe = pipeline("text-generation", model="UUFO-Aigis/PicoLAiNN-100M-SmallDataset")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UUFO-Aigis/PicoLAiNN-100M-SmallDataset")
model = AutoModelForCausalLM.from_pretrained("UUFO-Aigis/PicoLAiNN-100M-SmallDataset")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UUFO-Aigis/PicoLAiNN-100M-SmallDataset with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UUFO-Aigis/PicoLAiNN-100M-SmallDataset"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UUFO-Aigis/PicoLAiNN-100M-SmallDataset",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/UUFO-Aigis/PicoLAiNN-100M-SmallDataset

SGLang

How to use UUFO-Aigis/PicoLAiNN-100M-SmallDataset 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 "UUFO-Aigis/PicoLAiNN-100M-SmallDataset" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UUFO-Aigis/PicoLAiNN-100M-SmallDataset",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "UUFO-Aigis/PicoLAiNN-100M-SmallDataset" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UUFO-Aigis/PicoLAiNN-100M-SmallDataset",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use UUFO-Aigis/PicoLAiNN-100M-SmallDataset with Docker Model Runner:
```
docker model run hf.co/UUFO-Aigis/PicoLAiNN-100M-SmallDataset
```

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Check out the documentation for more information.

Pico-OpenLAiNN-testing 🤗

Hey there fellow researchers, developers, and AI enthusiasts! Today I'm releasing a smol open LLM. This is mainly just a test and I plan to release actually usable models in the near future.

Models Overview

Pico-OpenLAiNN-100M-SmallData: The smallest of the bunch, this 100M parameter model is perfect for quick experiments and applications where computational resources are extremely limited.

Pretraining Details

This specific version of Pico LAiNN was trained on just 8 billion tokens of the fineweb dataset.

Other information:

Compatibility: Built to be compatible with existing projects that use LLAMA 2's tokenizer and architecture.
Ease of Use: No need to reinvent the wheel. These models are ready to be plugged into your applications.
Open Source: Fully open source, so you can tweak, tune, and twist them to your heart's content.

Getting Started

To start using these models, you can simply load them via the Hugging Face transformers library:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer


MODEL_NAME = "UUFO-Aigis/Pico-OpenLAiNN-100M" #Replace 100M with 250M or 500M if you prefer those models.

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)

def generate_text(prompt, model, tokenizer, max_length=512, temperature=1, top_k=50, top_p=0.95):
    inputs = tokenizer.encode(prompt, return_tensors="pt")

    outputs = model.generate(
        inputs,
        max_length=max_length,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        do_sample=True
    )


    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

def main():
    # Define your prompt
    prompt = "According to all known laws of aviation, there is no way a bee should be able to fly."

    generated_text = generate_text(prompt, model, tokenizer)

    print(generated_text)

if __name__ == "__main__":
    main()

Benchy :3

Tasks	Value		Stderr
arc_challenge	0.1826	±	0.0113
arc_easy	0.3859	±	0.0100
boolq	0.5804	±	0.0086
hellaswag	0.2791	±	0.0045
lambada_openai	0.2437	±	0.0060
piqa	0.6159	±	0.0113
winogrande	0.5067	±	0.0141

Future Plans

More Models: I'm currenetly training the bigger siblings of this models, including a 1B parameter version and beyond. 2-4 Billion parameter versions are planned.
New architecture: This is still up in the air and I'm still developing it, and will release if I deem it to be actually useful, so stay tuned!
Paper: A detailed paper will be posted at some point.

Credit Where Credit's Due

If you find these models useful and decide to use these models, a link to this repository would be highly appreciated. I am a one man show running this. Thanks 🤗

Downloads last month: 5

Safetensors

Model size

0.1B params

Tensor type

F16