---
license: other
language:
- de
task_categories:
- text-generation
tags:
- german
- deutsch
- sft
- instruction-tuning
- gemma
- lora
- chat
pretty_name: Gemma-4-E4B German SFT Corpus (4 controlled variants)
size_categories:
- 10K<n<100K
configs:
- config_name: v1
  data_files: gemma4_sft_de_v1.jsonl
- config_name: v2
  data_files: gemma4_sft_de_v2.jsonl
- config_name: v3
  data_files: gemma4_sft_de_v3.jsonl
- config_name: v4
  data_files: gemma4_sft_de_v4.jsonl
---

# Gemma-4-E4B German SFT Corpus — 4 controlled variants

Curated, **native-heavy** German supervised-fine-tuning (SFT) corpus, built to improve the
**general German skill** of `unsloth/gemma-4-E4B-it` via LoRA — **NOT** to target any single
benchmark. The EuroEval-ported German benchmarks (`scala_de`, `sb10k_de`, `include_de`,
`mmlu_prox_de`, `germeval_de`, `germanquad_de`, …) are used only as honest thermometers, never
as training signal — no benchmark train/test split is mixed in, deliberately, to avoid
Goodharting.

Four variants share one instruction/response backbone and differ **only** along three
controlled axes, so that one LoRA per variant isolates each axis's effect.

| Variant | MCQ target | Answer balance | Replay | Rows   | Sources | Isolates              |
|---------|-----------|----------------|--------|--------|---------|-----------------------|
| **v1**  | reasoning | balanced       | ✅ ~8% | 27 996 | 12      | full recipe           |
| **v2**  | terse     | balanced       | ❌     | 25 756 | 11      | terse-vs-reasoning    |
| **v3**  | reasoning | balanced       | ❌     | 25 756 | 11      | replay effect (vs v1) |
| **v4**  | reasoning | **unbalanced** | ✅ ~8% | 27 996 | 12      | balance effect (vs v1)|

**Designed contrasts** (hold everything else equal):

- **v1 ↔ v3** → *replay* effect (raw-German continuation as an anti-forgetting regularizer)
- **v1 ↔ v4** → *answer-balance* effect (shuffling MCQ gold positions to kill the "default-to-A" bias)
- **v2 ↔ v3** → *terse-vs-reasoning* MCQ target format

## Format

ShareGPT-style JSON Lines, one conversation per line:

```json
{
  "source": "maxidl/OpenOrca-gpt4-de",
  "conversations": [
    {"from": "human", "value": "..."},
    {"from": "gpt",   "value": "..."}
  ]
}
```

- **Strict role alternation** `human → gpt → human → gpt …`, starting at `human`, ending at
  `gpt`. Required by the Gemma-4 chat template, which rejects a `system` role and refuses
  consecutive same-role turns ("Conversation roles must alternate"). During the build, system
  turns were merged into the first user turn and consecutive same-role turns were collapsed.
- `source` records the upstream dataset, or a synthetic bucket: `replay_de`, `m_arc_de`,
  `mmlu_aux_de`.

## Composition

Each variant = a **shared bulk** of 24 640 instruction/response rows (after dedup + per-source
cap), **+ 1 116 MCQ** rows (`m_arc_de`), **+ 2 240 replay** rows for v1/v4. Per-source counts
(`v1` / `v4`; `v2` / `v3` are identical minus the 2 240 `replay_de` rows):

| Rows  | Source                                   | Kind |
|-------|------------------------------------------|------|
| 3 485 | FreedomIntelligence/alpaca-gpt4-deutsch  | high-quality MT (GPT-4) |
| 3 484 | toroe/Soofi-Think-SFT-V2-firsthalf-DE     | reasoning (math/science) |
| 3 483 | CausalLM/GPT-4-Self-Instruct-German       | native German |
| 3 482 | flozi00 (`/conversations`)                | native German |
| 3 480 | stefan-it/secret-german-reasoning         | native German reasoning |
| 3 477 | maxidl/Capybara-de                        | high-quality MT |
| 3 460 | maxidl/OpenOrca-gpt4-de                    | high-quality MT (GPT-4) |
| 2 240 | `replay_de` — raw German text (Wikipedia), text-continuation (v1/v4 only) | replay |
| 1 116 | `m_arc_de` — `alexandrainst/m_arc` (de), balanced MCQ | MCQ |
| 268   | LeoLM/OpenSchnabeltier                    | native German |
| 17    | `aya_de` — `CohereLabs/aya_dataset` (de)   | native German |
| 4     | `mmlu_aux_de` — MMLU auxiliary-train (de)  | MCQ aux |

> **Round-1 residue:** `aya_de` (17) and `mmlu_aux_de` (4) are small because streaming missed
> most German `aya` rows and `mmlu_aux` collapsed on final-turn dedup. Retained for
> transparency; a round-2 rebuild will restore them.

## Curation

- **Native-heavy:** native German prioritized; high-quality MT (DeepL'24 / GPT-4 translations)
  admitted; noisy MT excluded.
- **Language filter:** `lingua` German language-ID where available, with a stop-word + length
  heuristic fallback when `lingua` is absent.
- **Per-source 20 % cap + round-robin mixing** → no single source dominates. For the target of
  28 000 the cap is 5 600 rows/source; in practice the 7 large sources each land at ~3 480 rows
  (well under the cap) because the round-robin draws evenly across them.
- **Global dedup** on the final `gpt` turn.
- **Regularized approximate replay** (`replay_de`, v1/v4 only): ~8.7 % raw-German Wikipedia
  text as `human` (passage start) → `gpt` (continuation), to counter catastrophic forgetting of
  base German fluency. Replay fraction = `n · ratio / (1 − ratio)`.
- **MCQ handling** (`m_arc_de`): *balanced* variants shuffle the gold-answer position uniformly
  over A/B/C/D (kills positional / A-default bias); the *unbalanced* variant (v4) keeps original
  positions. *terse* target = `"Die korrekte Antwort ist (X)."`; *reasoning* target = short
  rationale then the letter.
- **Explicitly excluded:** `slimorca_dedup_german_experimental` (noisy MT),
  `ultra_chat_de` (verbosity).

## Contamination

Independent of the held-out benchmarks (INCLUDE-de, EuroEval ScaLA / SB10k / GermEval /
GermanQuad, MMLU-ProX / MMMLU-de). No benchmark test split is included; `openai/MMMLU[DE]` and
`LeoLM/MMLU_de` (eval-only) were deliberately **not** used.

## Intended use & trained models

LoRA SFT of `unsloth/gemma-4-E4B-it`: r128 / α256 · bf16 · lr 5e-5 (gentle) · effective batch
16 · 1 epoch · reasoning-off · seed 3407 · 1× H100. Models trained from these variants:

- [`peerbench/gemma-4-E4B-20260610190427-v1`](https://huggingface.co/peerbench/gemma-4-E4B-20260610190427-v1) — reasoning + balanced + replay
- [`peerbench/gemma-4-E4B-20260610190427-v2`](https://huggingface.co/peerbench/gemma-4-E4B-20260610190427-v2) — terse + balanced
- [`peerbench/gemma-4-E4B-20260610190427-v3`](https://huggingface.co/peerbench/gemma-4-E4B-20260610190427-v3) — reasoning + balanced, no replay
- [`peerbench/gemma-4-E4B-20260610190427-v4`](https://huggingface.co/peerbench/gemma-4-E4B-20260610190427-v4) — reasoning + unbalanced + replay

## Provenance & licensing

Built by re-formatting and filtering the upstream datasets listed above; **each retains its
original license / terms** — consult the source repositories before redistribution. Synthetic
buckets (`replay_de`, `m_arc_de`, `mmlu_aux_de`) are transformations of publicly available
German text / ARC / MMLU-auxiliary material. Provided **as-is, for research**; `license: other`
reflects the mixed upstream terms.

## Build

- Builder: `build_gemma_sft_variants.py` — `--target 28000 --seed 3407`.
- Built 2026-06-10 on the German-LLM GPU client host (`gaa-mdk`): ShareGPT normalization →
  language filter → per-source cap → dedup → strict-alternation pass → variant assembly.

---
*Part of the peerbench German-LLM fine-tuning experiments.*