Instructions to use ai-toba/toba-trilingual-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ai-toba/toba-trilingual-1.2B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ai-toba/toba-trilingual-1.2B", filename="gguf/models/toba-sft-chat-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ai-toba/toba-trilingual-1.2B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ai-toba/toba-trilingual-1.2B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ai-toba/toba-trilingual-1.2B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ai-toba/toba-trilingual-1.2B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ai-toba/toba-trilingual-1.2B:Q4_K_M
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 ai-toba/toba-trilingual-1.2B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ai-toba/toba-trilingual-1.2B:Q4_K_M
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 ai-toba/toba-trilingual-1.2B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ai-toba/toba-trilingual-1.2B:Q4_K_M
Use Docker
docker model run hf.co/ai-toba/toba-trilingual-1.2B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ai-toba/toba-trilingual-1.2B with Ollama:
ollama run hf.co/ai-toba/toba-trilingual-1.2B:Q4_K_M
- Unsloth Studio
How to use ai-toba/toba-trilingual-1.2B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ai-toba/toba-trilingual-1.2B to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ai-toba/toba-trilingual-1.2B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ai-toba/toba-trilingual-1.2B to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ai-toba/toba-trilingual-1.2B with Docker Model Runner:
docker model run hf.co/ai-toba/toba-trilingual-1.2B:Q4_K_M
- Lemonade
How to use ai-toba/toba-trilingual-1.2B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ai-toba/toba-trilingual-1.2B:Q4_K_M
Run and chat with the model
lemonade run user.toba-trilingual-1.2B-Q4_K_M
List all available models
lemonade list
This TOBA model is a trilingual language model based on GPT-2 architecture with 1.2 billion parameters, trained on a corpus encompassing Indonesian, Batak, and Minangkabau using syllabic-agglutinative tokenization. The architecture integrates an Engram Memory mechanism, an adaptive n-gram-based memory system with a 500,000 x 768 embedding table that captures morphological dependencies through bigram and trigram pathways.
Model file:
model.safetensors
Install
Install PyTorch first according to your CPU/CUDA environment, then install the repo requirements:
pip install -r requirements.txt
Usage
Single prompt, chat mode:
python infer.py --mode chat --prompt "Horas amang inang saluhutna"
Single prompt, completion mode:
python infer.py --mode completion --prompt "Horas amang inang saluhutna"
Interactive:
python infer.py --interactive --mode chat
Quick examples:
python infer.py --prompt "Horas!"
python infer.py --mode completion --prompt "Patorang ma aha do dalihan natolu "
python infer.py --interactive
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