---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
  - semiconductor
  - fab-process
  - infineon
  - industrial-ai
  - sequence-modeling
---

# XCombinator — sft-fab-scale-2000

> ⚠️ **Post-deadline upload notice.** This Hugging Face repository was **published *after* the Zero One Hack_01 submission deadline (2026-05-31 10:00 CET)**, solely to give judges download access. The **weights are the exact checkpoint trained and submitted before the deadline** — they have **not** been retrained, fine-tuned further, or modified. Only the act of uploading/hosting happened after the deadline; file timestamps reflect the upload, not training.

Full fine-tune of **Qwen/Qwen2.5-1.5B-Instruct** on semiconductor wafer-fab **process logic** (Zero One Hack_01,
Industrial AI / Infineon track), team **XCombinator**. Data-scaling point — **2000 routes/family**, 1 epoch. **Completion specialist** (block-acc 0.735, beats the n-gram 0.637).

One of the checkpoints compared in our study; the flagship is
[`XCombinator/sft-fab-instruct-all`](https://huggingface.co/XCombinator/sft-fab-instruct-all).

## Prompt format

Unified JSON format: a system prompt (task + output schema) + a numbered user sequence → one JSON
answer (`{"reasoning": "...", "steps": [...]}` for next-step/completion; `{"reasoning": "...",
"valid": bool, "rule": "RULE_..."|null}` for anomaly). Build the exact messages with
`zo_train.prompts.build_messages` from the
[project repo](https://github.com/gardan4/Zero-One-XCombinator), then apply the tokenizer chat
template. See the flagship model card for a full `from_pretrained` snippet.

## Evaluation (MOSFET labeled eval, n≈200)

| task | this checkpoint | n-gram baseline |
|---|---|---|
| next-step (top-1) | 0.525 | 0.69 |
| sequence completion (block-acc) | 0.735 | 0.637 |
| anomaly (F1) | 0.108 | 0.89 |

Full study + all checkpoints: the project repo and `submissions/XCombinator/REPORT.md`.

## Notes

- Full fine-tune (not a LoRA adapter) — loads directly with `AutoModelForCausalLM.from_pretrained`.
- Trained on Leonardo (CINECA) A100 via a deterministic data factory over the organizer grammar.