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Model registry for Baguettotron vs Luth comparison app.
All 6 models with footprint data and size tiers for tab grouping.
"""
from dataclasses import dataclass
from typing import Literal
SizeTier = Literal["small", "medium", "large"]
@dataclass
class ModelEntry:
repo_id: str
name: str
author: str
params: int
params_display: str
file_size_mb: int
vram_estimate_mb: int
size_tier: SizeTier
description: str
architecture: str = "decoder"
license: str = "apache-2.0"
model_card_url: str = ""
# Baguettotron: 321M, ~642 MB (BF16)
# Luth models: from HF safetensors metadata where available; else params * 2 bytes
MODELS: list[ModelEntry] = [
ModelEntry(
repo_id="PleIAs/Baguettotron",
name="Baguettotron",
author="PleIAs",
params=320_956_992,
params_display="321M",
file_size_mb=642,
vram_estimate_mb=642,
size_tier="small",
description="321M generalist reasoning model, SYNTH, 80 layers",
model_card_url="https://huggingface.co/PleIAs/Baguettotron",
),
ModelEntry(
repo_id="kurakurai/Luth-LFM2-350M",
name="Luth-LFM2-350M",
author="kurakurai",
params=354_483_968,
params_display="0.4B",
file_size_mb=709,
vram_estimate_mb=709,
size_tier="small",
description="French fine-tuned LFM2-350M",
model_card_url="https://huggingface.co/kurakurai/Luth-LFM2-350M",
),
ModelEntry(
repo_id="kurakurai/Luth-0.6B-Instruct",
name="Luth-0.6B-Instruct",
author="kurakurai",
params=600_000_000,
params_display="0.6B",
file_size_mb=1200,
vram_estimate_mb=1200,
size_tier="medium",
description="Luth 0.6B Instruct",
model_card_url="https://huggingface.co/kurakurai/Luth-0.6B-Instruct",
),
ModelEntry(
repo_id="kurakurai/Luth-LFM2-700M",
name="Luth-LFM2-700M",
author="kurakurai",
params=700_000_000,
params_display="0.7B",
file_size_mb=1400,
vram_estimate_mb=1400,
size_tier="medium",
description="Luth LFM2 700M",
model_card_url="https://huggingface.co/kurakurai/Luth-LFM2-700M",
),
ModelEntry(
repo_id="kurakurai/Luth-LFM2-1.2B",
name="Luth-LFM2-1.2B",
author="kurakurai",
params=1_200_000_000,
params_display="1.2B",
file_size_mb=2400,
vram_estimate_mb=2400,
size_tier="large",
description="Luth LFM2 1.2B",
model_card_url="https://huggingface.co/kurakurai/Luth-LFM2-1.2B",
),
ModelEntry(
repo_id="kurakurai/Luth-1.7B-Instruct",
name="Luth-1.7B-Instruct",
author="kurakurai",
params=1_700_000_000,
params_display="1.7B",
file_size_mb=3400,
vram_estimate_mb=3400,
size_tier="large",
description="Luth 1.7B Instruct",
model_card_url="https://huggingface.co/kurakurai/Luth-1.7B-Instruct",
),
]
# Model IDs for inference (repo_id as key)
MODEL_IDS = [m.repo_id for m in MODELS]
# Group by size tier for tabs
TIER_ORDER: list[SizeTier] = ["small", "medium", "large"]
TIER_LABELS: dict[SizeTier, str] = {
"small": "~0.3–0.4B (Small)",
"medium": "~0.6–0.7B (Medium)",
"large": "~1–2B (Large)",
}
def get_models_by_tier() -> dict[SizeTier, list[ModelEntry]]:
out: dict[SizeTier, list[ModelEntry]] = {t: [] for t in TIER_ORDER}
for m in MODELS:
out[m.size_tier].append(m)
return out
def get_model_by_id(repo_id: str) -> ModelEntry | None:
for m in MODELS:
if m.repo_id == repo_id:
return m
return None
# GGUF Q4_K_M size (MB) and source per model — for consolidated comparison table
# Baguettotron: PleIAs/Baguettotron-GGUF (HF). Luth: LEAP bundle outputs.
MODEL_GGUF_REF: dict[str, tuple[str, str]] = {
"PleIAs/Baguettotron": ("240", "PleIAs/Baguettotron-GGUF"),
"kurakurai/Luth-LFM2-350M": ("219", "LEAP bundle"),
"kurakurai/Luth-LFM2-700M": ("447", "LEAP bundle"),
"kurakurai/Luth-LFM2-1.2B": ("697", "LEAP bundle"),
"kurakurai/Luth-0.6B-Instruct": ("378", "LEAP bundle"),
"kurakurai/Luth-1.7B-Instruct": ("1,056", "LEAP bundle"),
}
def footprint_table_data() -> list[list[str]]:
"""Single consolidated comparison: Model | Params | VRAM (MB) | Fits on phone | GGUF Q4_K_M (MB) | Source"""
phone_fit = "✓" # Luth
phone_no = "✗" # PleIAs
rows: list[list[str]] = []
for m in MODELS:
gguf_mb, source = MODEL_GGUF_REF.get(m.repo_id, ("—", "—"))
rows.append([
m.name,
m.params_display,
str(m.vram_estimate_mb),
phone_fit if m.author != "PleIAs" else phone_no,
gguf_mb,
source,
])
return rows
def combined_footprint() -> tuple[int, float]:
"""Total disk (MB) and total VRAM (GB) for all 6 models."""
total_disk = sum(m.file_size_mb for m in MODELS)
total_vram_mb = sum(m.vram_estimate_mb for m in MODELS)
return total_disk, total_vram_mb / 1024
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