Instructions to use nakamura196/yigdzin1-gguf 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 nakamura196/yigdzin1-gguf 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 nakamura196/yigdzin1-gguf # Run inference directly in the terminal: llama cli -hf nakamura196/yigdzin1-gguf
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nakamura196/yigdzin1-gguf # Run inference directly in the terminal: llama cli -hf nakamura196/yigdzin1-gguf
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 nakamura196/yigdzin1-gguf # Run inference directly in the terminal: ./llama-cli -hf nakamura196/yigdzin1-gguf
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 nakamura196/yigdzin1-gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf nakamura196/yigdzin1-gguf
Use Docker
docker model run hf.co/nakamura196/yigdzin1-gguf
- LM Studio
- Jan
- Ollama
How to use nakamura196/yigdzin1-gguf with Ollama:
ollama run hf.co/nakamura196/yigdzin1-gguf
- Unsloth Desktop
- Docker Model Runner
How to use nakamura196/yigdzin1-gguf with Docker Model Runner:
docker model run hf.co/nakamura196/yigdzin1-gguf
- Lemonade
How to use nakamura196/yigdzin1-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nakamura196/yigdzin1-gguf
Run and chat with the model
lemonade run user.yigdzin1-gguf-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Yigdzin-1 β GGUF (llama.cpp)
GGUF conversion of BDRC/tibetan-ocr
("Yigdzin-1"), a Tibetan OCR vision-language model fine-tuned from
PaddlePaddle/PaddleOCR-VL-1.6,
for use with llama.cpp / llama-server
(mtmd multimodal support). Used by the
local-ocr desktop app.
Files
yigdzin1.ggufβ the language model (Q8_0-equivalent precision inherited from the source checkpoint's conversion; same conversion path asPaddlePaddle/PaddleOCR-VL-1.6-GGUF)yigdzin1-mmproj.ggufβ the vision projector (--mmproj)
Conversion notes
Converted with a stock (unmodified) llama.cpp build (b10776) using its
conversion/ernie.py PaddleOCR-VL converter, with one compatibility fix: this
checkpoint ships processor_config.json with size.shortest_edge/size.longest_edge
instead of the flat min_pixels/max_pixels keys the base PaddleOCR-VL-1.6 uses,
which the stock converter doesn't handle. No change was made to inference-time
behavior β the produced GGUF carries no non-standard metadata and loads/runs
identically to any other PaddleOCR-VL-arch GGUF on stock llama-server.
(An earlier, separate experiment explored whether Yigdzin-1's image-token positional encoding needs a non-standard "sequential" mode at inference time β relevant for whole-page images. It was found unnecessary for the line-level crops this conversion targets; see the source app's repository for details.)
Usage
llama-server -m yigdzin1.gguf --mmproj yigdzin1-mmproj.gguf
Then send a chat-completion request with an image (line crop) and the prompt
"Extract all Tibetan text.".
License
Apache-2.0, inherited from BDRC/tibetan-ocr.
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