How to use from
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 robloxianer/babble:F32
# Run inference directly in the terminal:
llama cli -hf robloxianer/babble:F32
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf robloxianer/babble:F32
# Run inference directly in the terminal:
llama cli -hf robloxianer/babble:F32
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 robloxianer/babble:F32
# Run inference directly in the terminal:
./build/bin/llama cli -hf robloxianer/babble:F32
Use Docker
docker model run hf.co/robloxianer/babble:F32
Quick Links

Babble

A family of 5 tiny character-level GPTs trained from scratch on the same data (FineWeb-Edu + everyday conversations). They ramble. That's the point.

Model Params Layers d_model Context Val loss
Micro 0.11M 2 64 64 1.699
Tiny 0.82M 4 128 128 1.388
Small 2.74M 6 192 256 1.264
Medium 10.78M 6 384 256 1.167
Bigish 19.47M 8 448 256 1.18

Each model lives in its own subfolder with its own README (model card), the PyTorch weights (.pt) and a GGUF (.gguf).

  • Training / chat / export code: chiken_sandwich.py (one file does it all)
  • Chat with all models at once: python chiken_sandwich.py chat
  • Do not trust the output. They are toys.
Downloads last month
72
GGUF
Model size
19.5M params
Architecture
gpt2
Hardware compatibility
Log In to add your hardware

32-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support