Spaetzle-v69-7b

This is a progressive (mostly dare-ties, but also slerp) merge with the intention of a suitable compromise for English and German local tasks.

There is also a 4q_k_m quantized GGUF.

It should work sufficiently well with ChatML prompt template (for all merged models should have seen ChatML prompts at least in DPO stage).

Evaluation

Benchmark scores are not the possible optimum, as the model attempts a compromise with a number of parameters, like German language performance, instruction following, reasoning capabilities, robustness (so far, i did not encounter inserted tokens, e.g.), model licensing, and other criteria. Nevertheless, they are not too bad:

It achieves (running quantized) in

• German EQ Bench: Score (v2_de): 62.59 (Parseable: 171.0).
• English EQ Bench: Score (v2): 76.43 (Parseable: 171.0).

Open LLM Leaderboard Evaluation Results: Detailed results can be found here

Nous benchmark results:

AGIEval

Average: 44.48%

GPT4All

Average: 75.84%

TruthfulQA

Average: 66.15%

Bigbench

Average: 46.59%

Average score: 58.27%

🧩 Merge Configuration

Spaetzle-v69-7b is a merge of the following models using LazyMergekit:

• abideen/AlphaMonarch-dora
• cstr/Spaetzle-v68-7b

The merge tree in total involves the following original models:

• abideen/AlphaMonarch-dora
• mayflowergmbh/Wiedervereinigung-7b-dpo
• flemmingmiguel/NeuDist-Ro-7B
• ResplendentAI/Flora_DPO_7B
• yleo/EmertonMonarch-7B
• occiglot/occiglot-7b-de-en-instruct
• OpenPipe/mistral-ft-optimized-1227
• DiscoResearch/DiscoLM_German_7b_v1
• LeoLM/leo-mistral-hessianai-7b
• DRXD1000/Phoenix
• VAGOsolutions/SauerkrautLM-7b-v1-mistral
• malteos/hermeo-7b
• FelixChao/WestSeverus-7B-DPO-v2
• cognitivecomputations/openchat-3.5-0106-laser

For this last merge:

models:
  - model: cstr/Spaetzle-v68-7b
    # no parameters necessary for base model
  - model: abideen/AlphaMonarch-dora
    parameters:
      density: 0.60
      weight: 0.30
merge_method: dare_ties
base_model: cstr/Spaetzle-v68-7b
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/Spaetzle-v69-7b"
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. Of the 14 constituent models this card names, 13 are still published and 1 are not: VAGOsolutions/SauerkrautLM-7b-v1-mistral. For those, the training-content chain cannot be followed from this card, and no summary is reconstructed here in their place — an untraceable summary presented as a traceable one would be worse than the gap.