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 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
- 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
File size: 3,949 Bytes
20af279 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | #!/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")
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