Instructions to use Skull18500/Biggerbrain-118-million with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Skull18500/Biggerbrain-118-million with Transformers:

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

pipe = pipeline("text-generation", model="Skull18500/Biggerbrain-118-million")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Skull18500/Biggerbrain-118-million", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Skull18500/Biggerbrain-118-million with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Skull18500/Biggerbrain-118-million"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Skull18500/Biggerbrain-118-million",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Skull18500/Biggerbrain-118-million

SGLang

How to use Skull18500/Biggerbrain-118-million 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 "Skull18500/Biggerbrain-118-million" \
    --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": "Skull18500/Biggerbrain-118-million",
		"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 "Skull18500/Biggerbrain-118-million" \
        --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": "Skull18500/Biggerbrain-118-million",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Skull18500/Biggerbrain-118-million with Docker Model Runner:
```
docker model run hf.co/Skull18500/Biggerbrain-118-million
```

Biggerbrain AI 118 million parameter model is the full title of this model. It is based on the MoLaMart Architecture invented by me. MoLaMart stands for Mixture of Layers(Dynamic FFN block switching), and Memory augmented(GRU and custom memory module), Recurrent Transformer. (A transformer with at least 40% of its layers in an iteration loop.)

This is the space for testing the model's current capabilities: https://huggingface.co/spaces/Skull18500/BIggerbrain-118-million

The Github link for the project's complete files and features is: https://github.com/skull18500/BiggerBrain-AI

Im 13 y.o. trying to perfect this architecture on a 4060. Currently, my AI is very good at making 1 sentence or 2 sentence stories, due to too much Tinystories in the training data. It used a mixture of random data shuffling(data split into 511 token sequences, then shuffled) to make sure it didnt memorize the order. It has 6.1 Billion training tokens, but it has estimated only seen 1-3 billion. I used A mixture of Tinystories, wikitext, Gutenberg, Etc, though some other sources were accidentally incorperated into the mix in small amounts.

Downloads last month: -; Downloads are not tracked for this model. How to track

Safetensors

Model size

0.2B params

Tensor type

F32

Skull18500
/

Biggerbrain-118-million

Space using Skull18500/Biggerbrain-118-million 1