Token Classification
GGUF
Safetensors
Chinese
input-method
zhuyin
bopomofo
traditional-chinese
ternary
bitnet
Instructions to use Luigi/sloth-ime-models 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 Luigi/sloth-ime-models 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 Luigi/sloth-ime-models # Run inference directly in the terminal: llama cli -hf Luigi/sloth-ime-models
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Luigi/sloth-ime-models # Run inference directly in the terminal: llama cli -hf Luigi/sloth-ime-models
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 Luigi/sloth-ime-models # Run inference directly in the terminal: ./llama-cli -hf Luigi/sloth-ime-models
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 Luigi/sloth-ime-models # Run inference directly in the terminal: ./build/bin/llama-cli -hf Luigi/sloth-ime-models
Use Docker
docker model run hf.co/Luigi/sloth-ime-models
- LM Studio
- Jan
- Ollama
How to use Luigi/sloth-ime-models with Ollama:
ollama run hf.co/Luigi/sloth-ime-models
- Unsloth Desktop
- Docker Model Runner
How to use Luigi/sloth-ime-models with Docker Model Runner:
docker model run hf.co/Luigi/sloth-ime-models
- Lemonade
How to use Luigi/sloth-ime-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Luigi/sloth-ime-models
Run and chat with the model
lemonade run user.sloth-ime-models-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Commit History
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predictor v2.1 full weights (TW-chat register FT)
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predictor v2.1 full weights (TW-chat register FT)
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card: decoder v2.1 section (register FT, honest evals) + repro pointer
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predictor v2.1: TW-chat register fine-tune — chat 10.9->18.3 top-1, fresh-C4 unchanged; drop-in
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predictor v2 full weights (6.1M-line corpus)
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predictor v2: retrained on 6.1M lines — honest fresh-corpus 34/46 vs old 4.6/12 (leak-inflated 47.3/75.8); drop-in
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retitle as the Sloth IME model-family repo (renamed from slothe-t-25m-zhuyin)
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plot: all-measured (homophone-hard vs on-device latency, 4 models, projection gone)
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decoder full weights: 60M dense-Qwen3.5 GDN next-word predictor (HF format, fp32 safetensors + tokenizer)
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Files: list 12M GGUF (shipping default), 12M fp32 master, 60M predictor
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12M weights: 12m/roles.json
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12M weights: 12m/slothe_config.json
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12M weights: 12m/model.safetensors
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add 60M Q4 next-word predictor (qwen35 GDN hybrid, 47.3/75.8 held-out, 8.5ms/word on BOOX @4t )
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add 12M ternary 256x12 sibling (9.65MB TQ2_0, 84%/84%, 9.3ms@4t on BOOX)
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latency correction: 25M ~9ms was projected; measured 18.5ms@4t/31.9ms@2t; add measured 12M sibling
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Render score-vs-latency chart as PNG for reliable HF display
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Add score-vs-latency chart; fix pipeline_tag to token-classification
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SlothE-T 25M ternary Zhuyin→zh-TW IME model: ternary GGUF (TQ2_0) + fp32 master + reproducibility toolchain
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initial commit
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