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.
- Libraries
- llama-cpp-python
How to use Luigi/sloth-ime-models with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Luigi/sloth-ime-models", filename="pred_q35_60m-q4.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- 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 Studio
How to use Luigi/sloth-ime-models 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 Luigi/sloth-ime-models 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 Luigi/sloth-ime-models to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Luigi/sloth-ime-models to start chatting
- Atomic Chat new
- 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
| #!/usr/bin/env python3 | |
| """Score-vs-latency scatter for the model card. Self-contained SVG, no deps.""" | |
| # honest held-out (500 fresh zh-TW sentences) + BOOX (SD662) per-6-syllable decode ms | |
| # latency: 4M/12M measured (ORT int8); 25M ternary projected from measured-shape | |
| # bitnet TQ2_0/I2_S kernel benchmarks (marked with *). | |
| MODELS = [ | |
| # name, params, latency_ms, proj, mianxuan, homophone, toneless, color | |
| ("4M int8", "4M", 9.06, False, 70, 83, 74, "#6b7280"), | |
| ("12M int8", "12M", 13.3, False, 72, 82, 79, "#2563eb"), | |
| ("25M ternary", "25M", 9.0, True, 76, 86, 77, "#dc2626"), | |
| ] | |
| W, H = 680, 440 | |
| L, R, T, B = 74, 28, 46, 66 # margins | |
| PX0, PX1 = L, W - R | |
| PY0, PY1 = T, H - B | |
| XMIN, XMAX = 8.0, 14.5 | |
| YMIN, YMAX = 66.0, 80.0 | |
| def x(v): return PX0 + (v - XMIN) / (XMAX - XMIN) * (PX1 - PX0) | |
| def y(v): return PY1 - (v - YMIN) / (YMAX - YMIN) * (PY1 - PY0) | |
| s = [] | |
| s.append(f'<svg xmlns="http://www.w3.org/2000/svg" width="{W}" height="{H}" ' | |
| f'viewBox="0 0 {W} {H}" font-family="-apple-system,Segoe UI,Roboto,sans-serif">') | |
| # always-light background card so it reads on both HF themes | |
| s.append(f'<rect x="0" y="0" width="{W}" height="{H}" rx="10" fill="#ffffff"/>') | |
| s.append(f'<text x="{L}" y="26" font-size="16" font-weight="700" fill="#111827">' | |
| f'Held-out quality vs. on-device latency (BOOX SD662)</text>') | |
| # gridlines + y ticks (ε ιΈε %) | |
| for yv in range(66, 81, 2): | |
| yy = y(yv) | |
| s.append(f'<line x1="{PX0}" y1="{yy:.1f}" x2="{PX1}" y2="{yy:.1f}" stroke="#e5e7eb"/>') | |
| s.append(f'<text x="{PX0-10}" y="{yy+4:.1f}" font-size="12" fill="#6b7280" ' | |
| f'text-anchor="end">{yv}</text>') | |
| # x ticks (ms) | |
| for xv in range(8, 15, 1): | |
| xx = x(xv) | |
| s.append(f'<line x1="{xx:.1f}" y1="{PY0}" x2="{xx:.1f}" y2="{PY1}" stroke="#f3f4f6"/>') | |
| s.append(f'<text x="{xx:.1f}" y="{PY1+20}" font-size="12" fill="#6b7280" ' | |
| f'text-anchor="middle">{xv}</text>') | |
| # axis labels | |
| s.append(f'<text x="{(PX0+PX1)/2:.0f}" y="{H-14}" font-size="13" fill="#374151" ' | |
| f'text-anchor="middle">latency β ms / 6-syllable decode (lower = faster)</text>') | |
| s.append(f'<text x="18" y="{(PY0+PY1)/2:.0f}" font-size="13" fill="#374151" ' | |
| f'text-anchor="middle" transform="rotate(-90 18 {(PY0+PY1)/2:.0f})">' | |
| f'ε ιΈε (whole-sentence) % (higher = better)</text>') | |
| # axis frame | |
| s.append(f'<rect x="{PX0}" y="{PY0}" width="{PX1-PX0}" height="{PY1-PY0}" ' | |
| f'fill="none" stroke="#9ca3af"/>') | |
| # Pareto arrow: 4M -> 25M ternary (up, same latency), the headline story | |
| s.append(f'<line x1="{x(9.06):.1f}" y1="{y(70)-10:.1f}" x2="{x(9.0):.1f}" ' | |
| f'y2="{y(76)+12:.1f}" stroke="#dc2626" stroke-width="1.5" ' | |
| f'stroke-dasharray="4 3" opacity="0.6"/>') | |
| # points | |
| for name, tag, lat, proj, mx, ho, tl, col in MODELS: | |
| cx, cy = x(lat), y(mx) | |
| s.append(f'<circle cx="{cx:.1f}" cy="{cy:.1f}" r="8" fill="{col}" ' | |
| f'stroke="#ffffff" stroke-width="2"/>') | |
| star = "*" if proj else "" | |
| # two-line label fully above the point (name higher, stats just above point), | |
| # except 4M which sits low so its label goes below the point. | |
| if name == "4M int8": | |
| name_y, sub_y = cy + 24, cy + 39 | |
| else: | |
| name_y, sub_y = cy - 30, cy - 14 | |
| s.append(f'<text x="{cx:.1f}" y="{name_y:.1f}" font-size="13" font-weight="700" ' | |
| f'fill="{col}" text-anchor="middle">{name}{star}</text>') | |
| s.append(f'<text x="{cx:.1f}" y="{sub_y:.1f}" font-size="11" fill="#6b7280" ' | |
| f'text-anchor="middle">ε {mx} Β· ε{ho} Β· η‘{tl}</text>') | |
| # projection note under the title (avoids the crowded bottom axis area) | |
| s.append(f'<text x="{L}" y="42" font-size="11" fill="#9ca3af">' | |
| f'* 25M ternary latency projected from measured-shape TQ2_0 / I2_S kernels</text>') | |
| s.append('</svg>') | |
| open("score_vs_latency.svg", "w").write("\n".join(s)) | |
| print("wrote score_vs_latency.svg") | |