Instructions to use kungaa/TuvanGemma 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 kungaa/TuvanGemma 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 kungaa/TuvanGemma:Q4_K_M # Run inference directly in the terminal: llama cli -hf kungaa/TuvanGemma:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kungaa/TuvanGemma:Q4_K_M # Run inference directly in the terminal: llama cli -hf kungaa/TuvanGemma: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 kungaa/TuvanGemma:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf kungaa/TuvanGemma: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 kungaa/TuvanGemma:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kungaa/TuvanGemma:Q4_K_M
Use Docker
docker model run hf.co/kungaa/TuvanGemma:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use kungaa/TuvanGemma with Ollama:
ollama run hf.co/kungaa/TuvanGemma:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use kungaa/TuvanGemma with Docker Model Runner:
docker model run hf.co/kungaa/TuvanGemma:Q4_K_M
- Lemonade
How to use kungaa/TuvanGemma with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kungaa/TuvanGemma:Q4_K_M
Run and chat with the model
lemonade run user.TuvanGemma-Q4_K_M
List all available models
lemonade list
- Atomic Chat
TuvanGemma
TuvanGemma is a family of local Russian ↔ Tuvan translation models. The
recommended release fine-tunes google/translategemma-12b-it and is provided
as a llama.cpp GGUF, a LoRA adapter, and a self-contained Windows application.
The models are Gemma-derived. Read and comply with the applicable Gemma Terms of Use before using or redistributing them.
Downloads
| Release | Download | Notes |
|---|---|---|
| 12B model (recommended) | tuvangemma-12b-q4_k_m.gguf | Q4_K_M, 7.31 GB; llama.cpp |
| 12B Windows app | TuvanTranslator-windows-x64-v0.2.1-12b.zip | Portable; bundles the model and CUDA, Vulkan, and CPU runtimes |
| 12B LoRA adapter | translategemma-12b-qlora-r16 | Use with google/translategemma-12b-it |
| 4B model (smaller legacy release) | tuvangemma-4b-q5_k_m.gguf | Q5_K_M, 2.84 GB |
| 4B Windows app | TuvanTranslator-windows-x64-v0.1.0.zip | Older, lower-memory portable app |
| 4B LoRA adapter | gemma4-e4b-qlora | Adapter used for the legacy release |
The v0.2.1 desktop app adds a Lower memory (2K context) switch. Leave it off for the normal 4,096-token context; turn it on before loading the model on an 8 GB GPU. Changing it restarts the local inference runtime automatically.
12B evaluation
The 12B adapter was evaluated on 256 held-out examples in each direction (512 translations total):
| Direction | Examples | BLEU | chrF++ |
|---|---|---|---|
| Russian → Tuvan | 256 | 20.4171 | 49.1626 |
| Tuvan → Russian | 256 | 25.9565 | 49.3654 |
The evaluation report describes the
procedure. Raw predictions are also included under reports/.
These are automatic metrics on one held-out split, not a guarantee of translation quality. Review names, numbers, idioms, and important text with a qualified Tuvan speaker.
GPU memory
Direct llama.cpp Vulkan measurements with full layer offload used 7,853 MiB at 4,096 tokens and 8,113 MiB at 8,192 tokens. In practice:
- 12 GB VRAM: comfortable for the 12B Q4 model at the default context.
- 8 GB VRAM: possible but tight; use the app's 2K context switch and close other GPU-heavy programs. Driver/display overhead varies by machine.
- Less than 8 GB: use partial offload/CPU or the smaller 4B release.
See the VRAM report for the measured configuration and caveats.
Previous 4B evaluation
The earlier 4B release was evaluated separately on 3,824 held-out examples in each direction. Its split and setup differ from the 12B evaluation, so the tables should not be compared as a controlled model-size experiment.
| Direction | Baseline chrF++ | TuvanGemma chrF++ | Baseline BLEU | TuvanGemma BLEU |
|---|---|---|---|---|
| Russian → Tuvan | 13.0657 | 45.4822 | 1.1298 | 16.2996 |
| Tuvan → Russian | 19.6637 | 44.7172 | 2.1083 | 20.8963 |
Dataset acknowledgment
Fine-tuning used Agisight's
tyv-rus-200k
dataset, attributed under CC BY 4.0. The desktop package includes the dataset
attribution notice.
Desktop application
Source code, build instructions, third-party notices, and the complete Gemma
terms are available in
kungaa/TuvanTranslator.
The Windows ZIP is self-contained and uses llama.cpp. Auto mode tries CUDA,
then Vulkan, then CPU. Vulkan requires a working Vulkan-capable graphics
driver; vulkan-1.dll is intentionally supplied by the driver rather than
bundled. A Metal backend and macOS packaging recipe are present in the source,
but a signed/notarized macOS binary is not currently published.
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Model tree for kungaa/TuvanGemma
Base model
google/translategemma-12b-it