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FLvdW/Zeroth42-4B
Zeroth42-4B is a machine learning model from FLvdW. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Safety-Certified Linux Installer Co-Pilot — a 4B assistant that guides non-technical users through installing a Void Linux-based OS (42 OS), with a guardrail that validates every recommendation before it reaches the d…
Downloads · 30 days
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98% of all-time downloads
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.gguf2.5 GB · 100%
From the Hugging Face model README
Safety-Certified Linux Installer Co-Pilot — a 4B assistant that guides non-technical users through installing a Void Linux-based OS (#42 OS), with a guardrail that validates every recommendation before it reaches the disk.
Brand: Zeroth42 (Asimov's Zeroth Law — safety as an unbreakable law of the system). Repos:
FLvdwl/Zeroth42-4B(ModelScope),FLvdW/Zeroth42-4B(HF).
| File | Purpose |
|---|---|
Zeroth42-4B-Q4_K_M.gguf | The model (Q4_K_M, 2.49 GB, llama.cpp) — sha256 4022f79f145afdb230388feff651a42d8448aa75bdfe142b5dc475d8a86243e3 |
MODEL_CARD.md | Full model card (EN + 中文), the source of truth for claims |
certification.md | Rung 1 safety certification (guardrail logic 48/48, 29/29 golden, 8/11 clean) |
hardware-matrix.md | Rung 2: MI50 / RX 9060 XT / CPU-only latency + answer consistency |
eval/ | Raw eval runs: the fine-tuned model ×2 + honest OOTB baseline (N=3 each at temp 0.2) + golden-table receipt |
scripts/rung1_certify.py | Reproducible certification (serve → guardrail-validate) |
scripts/rung2_matrix.py | Reproducible hardware matrix |
scripts/rules.json | Guardrail rules — the single source of truth for the #42 OS installer guardrail |
chat_template.jinja | The embedded training-format template (### Instruction:) |
config.json | Model architecture config |
LICENSE | Apache-2.0 (base + this model's weights) |
MANIFEST.txt | sha256 of every file |
| Metric | This model | Base (Qwen3.8-4B-Distilled) |
|---|---|---|
| Installer-QA total /69 | 44.0 | 33.5 |
| Decision-critical /25 | 14.0 | 12.5 |
| Golden-table safety audit | 29/29 (receipt: eval/golden-table-audit.md) | — |
| Guardrail model-output | 8/11 clean (2 fail-safe BLOCKs, 1 real self-contradiction caught) | — |
N=3 samples per question, temperature 0.2, two independent runs — both scored 44.0 (identical scores across runs). The fine-tuned model is +10.5 better than base on the installer register — an earlier eval was inverted by a chat-template bug; we found it (four-way model consultation), fixed it, and publish the corrected numbers openly.
llama-server -m Zeroth42-4B-Q4_K_M.gguf -c 8192 -ngl 99
The training-format chat template is embedded in the GGUF — no serve flags needed. CPU-only works (~3.7 s/question). Full details in MODEL_CARD.md.
Scope: the certified system is the co-pilot — this GGUF behind the guardrail (scripts/rules.json). Served standalone, it answers installer questions only; it cannot see your system, and out-of-scope questions (e.g. "which media player is installed?") may be answered confidently but wrongly.
Zeroth42-4B:过了安全认证的 Linux 安装助手(基于 Qwen3.8-4B-Distilled,Apache-2.0)
使用范围: 这个模型只回答安装问题,并且要在护栏后面用。单独跑它时,它看不到你的系统——超出安装范围的问题(比如“我机器上装了哪个播放器?”)可能会自信地答错。带护栏的完整安装助手才是通过认证的产品。
评测说明: 聊天模板有过一个 bug,把底模分数抬高了。修完重测,对比反过来——微调模型其实一直比底模高 10.5 分。bug、修复过程、正确数字都公开。
路线图: v1.1(2026 年 9 月底):#42 OS 安装器实机演示视频;v2:混合语料训练,修复自我矛盾问题。
Runs on llama.cpp/GGUF (community stack; we operate it). Hardware anyone can buy used.