Instructions to use ai-toba/toba-multilingual-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-multilingual-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-multilingual-1.2B", filename="gguf/toba-sft-20mei-multilang-1.2b-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
- llama.cpp
How to use ai-toba/toba-multilingual-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-multilingual-1.2B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ai-toba/toba-multilingual-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-multilingual-1.2B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ai-toba/toba-multilingual-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-multilingual-1.2B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ai-toba/toba-multilingual-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-multilingual-1.2B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ai-toba/toba-multilingual-1.2B:Q4_K_M
Use Docker
docker model run hf.co/ai-toba/toba-multilingual-1.2B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ai-toba/toba-multilingual-1.2B with Ollama:
ollama run hf.co/ai-toba/toba-multilingual-1.2B:Q4_K_M
- Unsloth Studio
How to use ai-toba/toba-multilingual-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-multilingual-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-multilingual-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-multilingual-1.2B to start chatting
- Docker Model Runner
How to use ai-toba/toba-multilingual-1.2B with Docker Model Runner:
docker model run hf.co/ai-toba/toba-multilingual-1.2B:Q4_K_M
- Lemonade
How to use ai-toba/toba-multilingual-1.2B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ai-toba/toba-multilingual-1.2B:Q4_K_M
Run and chat with the model
lemonade run user.toba-multilingual-1.2B-Q4_K_M
List all available models
lemonade list
TOBA Multilingual 1.2B
This TOBA model is a multilingual language model based on GPT-2 architecture with 1.2 billion parameters, trained for Indonesian, Batak, Minangkabau, Javanese, and Sundanese using syllabic-agglutinative tokenization. The architecture integrates an Engram Memory mechanism, an adaptive n-gram-based memory system for capturing morphological dependencies through bigram and trigram pathways.
Model Files
Safetensors:
safetensors/
GGUF Q4:
gguf/toba-sft-20mei-multilang-1.2b-Q4_K_M.gguf
Install
Install PyTorch first according to your CPU/CUDA environment, then install the package requirements:
pip install -r requirements.txt
Safetensors Usage
Run from the repository root.
Interactive chat:
python infer.py --interactive --mode chat
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"
Exit interactive mode:
/q
GGUF Usage
Run from the repository root.
Interactive chat:
python gguf/infer_gguf.py --interactive
Single prompt:
python gguf/infer_gguf.py --prompt "siapa presiden pertama indonesia?"
If you are already inside the gguf folder:
python infer_gguf.py --interactive
python infer_gguf.py --prompt "siapa presiden pertama indonesia?"
PowerShell wrapper:
.\gguf\infer_gguf.ps1 -Interactive
.\gguf\infer_gguf.ps1 -Prompt "kapan indonesia merdeka?"
From Command Prompt, run the PowerShell wrapper like this:
powershell -ExecutionPolicy Bypass -File .\gguf\infer_gguf.ps1 -Interactive
Translation
Safetensors and GGUF both use translation_wrapper.py for explicit translation prompts:
python infer.py --prompt "Terjemahkan paragraf berikut dari bahasa batak ke bahasa indonesia:
Oii, ito, sungkun-sungkunmu on memang na pas! Molo gabe produser au di musik dangdut koplo, on ma rencana na adong di otakku: 1. Dangdut Koplo "Go Internasional": Unang sai holan di Jawa manang Sumatera, ito. Bahenonta ma dangdut koplo on boi dihagiot halak di mancanegara. Carana? Kolaborasi dohot musisi internasional. Bayangkon ma, adong remix dangdut koplo dohot sentuhan musik latin manang reggae. Mantap! 2. Video Klip Na Unik: Unang ma video klip na standar."
python gguf/infer_gguf.py --prompt "Terjemahkan paragraf berikut dari Bahasa Indonesian ke Bahasa jawa: Akun saya udah 2 Minggu tidak dapat diskon resto tapi pas bantuan malah kaya robot yang jawab"
Default Generation Settings
temperature=0.4
top_k=30
top_p=0.85
do_sample=1
repetition_penalty=1.2
no_repeat_ngram_size=3
max_new_tokens=200
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