---
tags:
- merge
- mergekit
- lazymergekit
- flemmingmiguel/NeuDist-Ro-7B
- Blizado/discolm-mfto-7b-german-v0.1
- ResplendentAI/Flora_DPO_7B
base_model:
- flemmingmiguel/NeuDist-Ro-7B
- Blizado/discolm-mfto-7b-german-v0.1
- ResplendentAI/Flora_DPO_7B
license: cc-by-sa-4.0
---

# Spaetzle-v12-7b

Spaetzle-v12-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B)
* [Blizado/discolm-mfto-7b-german-v0.1](https://huggingface.co/Blizado/discolm-mfto-7b-german-v0.1)
* [ResplendentAI/Flora_DPO_7B](https://huggingface.co/ResplendentAI/Flora_DPO_7B)
* on the basis of [mayflowergmbh/Wiedervereinigung-7b-dpo-laser](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo-laser) 

As expected, this is a little bit worse in general English tasks over [cstr/spaetzle-v8-7b](https://huggingface.co/cstr/spaetzle-v8-7b), but a tiny little bit better on German tasks, at least some: e.g. it reaches an EQ-Bench (de)
score of 64.81, but only

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.36|
|AI2 Reasoning Challenge (25-Shot)|65.96|
|HellaSwag (10-Shot)              |86.16|
|MMLU (5-Shot)                    |63.48|
|TruthfulQA (0-shot)              |57.84|
|Winogrande (5-shot)              |80.03|
|GSM8k (5-shot)                   |62.70|


|                            Model                             |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|--------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Spaetzle-v12-7b](https://huggingface.co/cstr/Spaetzle-v12-7b)|  42.64|   74.3|     58.44|   44.44|  54.95|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |24.02|±  |  2.69|
|                              |       |acc_norm|21.65|±  |  2.59|
|agieval_logiqa_en             |      0|acc     |36.10|±  |  1.88|
|                              |       |acc_norm|37.63|±  |  1.90|
|agieval_lsat_ar               |      0|acc     |24.35|±  |  2.84|
|                              |       |acc_norm|23.04|±  |  2.78|
|agieval_lsat_lr               |      0|acc     |48.82|±  |  2.22|
|                              |       |acc_norm|47.25|±  |  2.21|
|agieval_lsat_rc               |      0|acc     |60.59|±  |  2.98|
|                              |       |acc_norm|57.99|±  |  3.01|
|agieval_sat_en                |      0|acc     |76.21|±  |  2.97|
|                              |       |acc_norm|74.76|±  |  3.03|
|agieval_sat_en_without_passage|      0|acc     |46.60|±  |  3.48|
|                              |       |acc_norm|45.63|±  |  3.48|
|agieval_sat_math              |      0|acc     |37.27|±  |  3.27|
|                              |       |acc_norm|33.18|±  |  3.18|

Average: 42.64%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |59.13|±  |  1.44|
|             |       |acc_norm|61.26|±  |  1.42|
|arc_easy     |      0|acc     |83.67|±  |  0.76|
|             |       |acc_norm|80.89|±  |  0.81|
|boolq        |      1|acc     |87.83|±  |  0.57|
|hellaswag    |      0|acc     |66.45|±  |  0.47|
|             |       |acc_norm|84.63|±  |  0.36|
|openbookqa   |      0|acc     |37.40|±  |  2.17|
|             |       |acc_norm|45.80|±  |  2.23|
|piqa         |      0|acc     |82.15|±  |  0.89|
|             |       |acc_norm|83.13|±  |  0.87|
|winogrande   |      0|acc     |76.56|±  |  1.19|

Average: 74.3%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |42.59|±  |  1.73|
|             |       |mc2   |58.44|±  |  1.58|

Average: 58.44%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|55.26|±  |  3.62|
|bigbench_date_understanding                     |      0|multiple_choice_grade|64.77|±  |  2.49|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|37.60|±  |  3.02|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|32.31|±  |  2.47|
|                                                |       |exact_str_match      |21.45|±  |  2.17|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|31.00|±  |  2.07|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|22.43|±  |  1.58|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|53.00|±  |  2.89|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|40.40|±  |  2.20|
|bigbench_navigate                               |      0|multiple_choice_grade|51.30|±  |  1.58|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|68.50|±  |  1.04|
|bigbench_ruin_names                             |      0|multiple_choice_grade|48.66|±  |  2.36|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|30.36|±  |  1.46|
|bigbench_snarks                                 |      0|multiple_choice_grade|70.17|±  |  3.41|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|70.39|±  |  1.45|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|31.00|±  |  1.46|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|21.44|±  |  1.16|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|18.29|±  |  0.92|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|53.00|±  |  2.89|

Average: 44.44%

Average score: 54.95%

Elapsed time: 02:50:51

## 🧩 Configuration

```yaml
models:
  - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
    # no parameters necessary for base model
  - model: flemmingmiguel/NeuDist-Ro-7B
    parameters:
      density: 0.60
      weight: 0.30
  - model: Blizado/discolm-mfto-7b-german-v0.1
    parameters:
      density: 0.65
      weight: 0.40
  - model: ResplendentAI/Flora_DPO_7B
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/Spaetzle-v12-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```

## EU AI Act Art. 53 — provider obligations

Added 2026-08-02 during an account-wide provenance review.

**This is a model merge, not a format conversion.** Most `cstr/*` repositories
are GGUF conversions, where the upstream research team remains the provider of
the model and the conversion changes only the numeric representation of the
weights. A merge produces a model that did not previously exist, so under
Regulation (EU) 2024/1689 the maintainer of this repository is plausibly the
**provider** of it, and the duties that survive the Art. 53(2)
free-and-open-source exemption — Art. 53(1)(c) and 53(1)(d) — attach here rather
than upstream.

**Art. 53(1)(c) — copyright policy.** No training corpus was assembled by this
repository. Merging combines weights that other providers already published; it
performs no text or data mining, so no rights reservation under Art. 4(3) of
Directive (EU) 2019/790 was engaged by this step. Copyright questions arising
from how the constituent models were themselves trained attach to their
respective providers. Any credible claim that this repository redistributes
material it has no right to redistribute will be acted on — contact via the
Community tab.

**Art. 53(1)(d) — training content.** No data was used to train this model: it
is a weight-space combination of models trained by others, and its training
content is theirs. All 3 constituent models this card names are still published, so the chain can be followed from here.

**Licence — resolved 2026-08-02.** `cc-by-sa-4.0`. `ResplendentAI/Flora_DPO_7B` is CC-BY-SA-4.0. **This card previously declared `apache-2.0`, which ShareAlike does not permit** — Apache-2.0 does not satisfy the obligation to license derivatives alike.

This was derived from the mergekit configuration reproduced in this card by
resolving each named constituent's licence on the Hub and taking the most
restrictive, rather than assumed from the model family. An earlier revision of
this section said the terms were unresolved; they are resolved now, and the
method is recorded so the conclusion can be checked rather than trusted.