Instructions to use sencool/Babypix1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sencool/Babypix1 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 sencool/Babypix1 # Run inference directly in the terminal: llama cli -hf sencool/Babypix1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sencool/Babypix1 # Run inference directly in the terminal: llama cli -hf sencool/Babypix1
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf sencool/Babypix1 # Run inference directly in the terminal: ./llama-cli -hf sencool/Babypix1
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf sencool/Babypix1 # Run inference directly in the terminal: ./build/bin/llama-cli -hf sencool/Babypix1
Use Docker
docker model run hf.co/sencool/Babypix1
- LM Studio
- Jan
- Ollama
How to use sencool/Babypix1 with Ollama:
ollama run hf.co/sencool/Babypix1
- Unsloth Desktop
- Docker Model Runner
How to use sencool/Babypix1 with Docker Model Runner:
docker model run hf.co/sencool/Babypix1
- Lemonade
How to use sencool/Babypix1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sencool/Babypix1
Run and chat with the model
lemonade run user.Babypix1-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Try babypix1 (Early Alpha) β Looking for testers and training feedback!
Hi everyone,
I just released babypix1, an early-stage, text-only LLM! π
It is currently in its initial training phase, so expect raw outputs, bugs, and room for growth. Iβm sharing it with the Hugging Face community early to gather feedback and improve the model.
How you can help:
Test it: Try running inference and see how it performs on different text tasks.
Give Feedback: Share what works, what fails, and where the responses break down.
Contribute Data: Suggest high-quality text datasets or instruction pairs to help with the next training run.
Try it out in the hosted inference API or download the weights, and leave your feedback in the replies below!